diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/ARCHIVE.md b/wizard-work/sphinx/archive-memgallery-2026-10-21/ARCHIVE.md new file mode 100644 index 0000000..34a19fe --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/ARCHIVE.md @@ -0,0 +1,210 @@ +# Mem-Gallery (arXiv:2601.03515v1) — archival report + +**Artifact:** `archive-memgallery-2026-10-21` +**Lane:** Archive — multimodal, long-context research +**Archive date:** 2026-10-21 (UTC) +**Wizard:** sphinx + +--- + +## 1. What was archived + +| | | +|---|---| +| paper | Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents | +| arXiv id | 2601.03515v1 | +| submitted | 2026-01-07 | +| authors | Bei, Wei, Ning, Zhao, Liu, Lin, Zhu, Hamann, He, Tong (UIUC; MIT-IBM Watson AI Lab / IBM Research; Stony Brook; Brookhaven) | +| comments | 34 pages, 18 figures | +| url | https://arxiv.org/abs/2601.03515 | +| code | https://github.com/YuanchenBei/Mem-Gallery | + +**Source snapshot (measured, 2026-10-21):** + +| file | bytes | sha256 | +|---|---|---| +| `raw/abs.html` | 43,359 | `022aed61b37e9161…` | +| `raw/api.xml` | 3,295 | `7e885f063961c934…` | +| `raw/paper.pdf` | 3,098,382 | `afb731ea1d9385e1…` | +| `paper.txt` (pdftotext -layout) | 265,932 | `1f1d233198f4e15a…` | +| `paper.raw.txt` (pdftotext -raw) | 144,480 chars | recorded in text_manifest.json | + +All three raw sources were fetched live over HTTPS on 2026-10-21 and hashed immediately; +`fetch_source.py` records URL, HTTP status, byte count and sha256 for each. Layer 1 of +`verify.py` re-hashes the bytes on disk against that manifest. + +--- + +## 2. What the paper is about + +Mem-Gallery is a benchmark for **multimodal long-term conversational memory** — memory +that has to carry *both* images and text across many sessions, not text alone and not a +single long context window. The authors' framing, quoted verbatim (Q01): + +> Long-term memory is a critical capability for multimodal large language model (MLLM) +> agents, particularly in conversational settings where information accumulates and evolves +> over time. However, existing benchmarks either evaluate multi-session memory in text-only +> conversations or assess multimodal understanding within localized contexts, failing to +> evaluate how multimodal memory is preserved, organized, and evolved across long-term +> conversational trajectories. + +**Dataset (Table 2, quoted as Q03):** + +| aspect | statistics | +|---|---| +| Conversation Data — Sessions | 240 | +| Conversation Data — Dialogue Rounds | 3,962 | +| Conversation Data — Included Images | 1,003 | +| Evaluation Data — QA Pairs with Annotated Clues | 1,711 | +| Evaluation Data — Included Images | 487 | + +The paper also states averages of **198.1 rounds per conversation** and **12.0 sessions per +conversation** (Figure 3). 3,962 / 240 = 16.51 rounds per conversation and 1,711 / 240 = 7.13 +QA pairs per conversation — both stated here so the reader can see the arithmetic rather than +take the averages on trust. **The relation between "198.1 rounds" and "3,962 dialogue rounds +over 240 sessions" is not derivable from the tabulated counts**; the paper does not define +"round" vs "session" explicitly enough to reconcile them. Reported as an open question, not +a defect claim. + +**Evaluation taxonomy (Q04, Q05)** — three families, nine subtasks: + +- *Memory Extraction & Adaptation*: Factual Retrieval, Visual-centric Search, Test-Time Learning +- *Memory Reasoning*: Temporal Reasoning, Visual-centric Reasoning, Multi-entity Reasoning +- *Memory Knowledge Management*: Knowledge Resolution, Conflict Detection, Answer Refusal + +Each QA pair carries **annotated evidence clues** naming the dialogue turns that support the +answer — which is what makes retrieval metrics (Recall/Precision@K) computable at all. + +--- + +## 3. Findings, and which of the paper's own numbers reproduce + +This is the part that matters to an archive: the paper states percentages in prose, and the +archive recomputed every one from the F1 values in its own Table 3. **The recomputation is a +measurement, and 6 of the 10 stated percentages do not reproduce.** Full detail in +`claims.json`; `verify_claims.py` re-runs it. + +### 3.1 Reproduces (4 of 10) + +| claim in prose | recomputed from Table 3 | verdict | +|---|---|---| +| MuRAG overall F1 gain over best textual (11.85%) | 11.85% | reproduced | +| MuRAG overall F1 gain over NGM (4.11%) | 4.11% | reproduced | +| MuRAG overall F1 gain over AUGUSTUS (5.39%) | 5.39% | reproduced | +| Full(MM) vs MuRAG overall F1 deficit (51.85%) | 51.85% | reproduced | + +The best textual model by Overall F1 is **A-Mem (0.6228)** — established here by direct +comparison, not quoted. + +### 3.2 Does not reproduce (6 of 10) + +| claim in prose | recomputed | what was tried | +|---|---|---| +| FR gain over best textual (7.69%) | **14.90%** (vs NaiveRAG) | no model in the FR row yields 7.69% | +| VS gain over best textual (12.29%) | **16.15%** (vs NaiveRAG) | no model in the VS row yields 12.29% | +| TTL gain over best textual (29.06%) | **25.30%** (vs NaiveRAG) | no model in the TTL row yields 29.06% | +| Full(MM) vs Full(Text) deficit (8.08%) | **7.48%** relative; 2.71 absolute points | neither relative nor absolute | +| A-Mem refusal deficit vs FIFO (7.22%) | **3.16%** relative; 3.03 absolute points | neither relative nor absolute | +| MuRAG refusal deficit vs FIFO (5.82%) | **1.70%** relative; 1.63 absolute points | neither relative nor absolute | + +`diag_denominators.py` brute-forces every model in each row as the denominator, so "none +matches" is a measured result rather than an assumption: for FR, VS and TTL the script prints +the full candidate list and no entry reproduces the stated figure. + +**Two readings are consistent with this, and the archive does not choose between them:** + +1. The prose percentages were computed against a different baseline than the printed table + shows (e.g. an earlier run, or a subset). +2. The prose percentages are arithmetic errors. + +Either way, **the four that do reproduce are all "MuRAG beats X" claims where X is the best +textual or the named structured baseline**; the six that fail are spread across task-level +gains, the Full(MM) deficit and the refusal trade-off. The headline findings in §4 do not +depend on the failing figures — they depend on table values, which are internally consistent +with the four reproduced claims. + +### 3.3 Findings taken from the table (not from prose percentages) + +- **MuRAG has the best Overall F1** (0.6966), ahead of UniversalRAG (0.6827), NGM (0.6691), + AUGUSTUS (0.6610) and every textual baseline (best: A-Mem 0.6228). +- **Simplicity beats structure.** MuRAG and UniversalRAG "simply preserve multimodal + information without structured memory organizations" and outperform NGM/AUGUSTUS, which + organise multimodally but are structured (Q07). +- **Naive multimodal accumulation hurts.** Full(MM) scores 0.3354 Overall F1 — below + Full(Text) at 0.3625, and far below MuRAG at 0.6966 (Q08). The paper's explanation is token + pressure: "naïvely accumulating multimodal content can introduce irrelevant visual noise, + crowd out informative text, and thus hurt performance." +- **Reasoning is where multimodality does not pay.** "even in the visual-centric reasoning + task that explicitly relies on visual clues, MemGPT, a textual memory method, can achieve + near-optimal performance" (Q09). +- **Refusal trades off against memory strength.** Weaker retrievers default to refusing; + A-Mem and MuRAG refuse less well than FIFO (Q10). +- **Efficiency: multimodal memory costs more than textual**, and "even a relatively simple + multimodal memory like MuRAG can approach the computational cost of highly sophisticated + textual memory systems" (Q11). +- **Retrieval coverage ≠ QA performance.** Recall rises with K while Precision falls + sharply; "the expanded retrieval coverage does not consistently translate into better QA + performance" (Q12). +- **Stated limitations (Q14):** vision–language only — no audio or embodied signals; no + planning or tool use. + +--- + +## 4. Provenance of every claim in this report + +| claim | where it comes from | +|---|---| +| title, authors, date, page count | `raw/abs.html` meta tags, live-fetched and hash-pinned | +| abstract, taxonomy, findings text | `quotes.json` Q01–Q14 — re-cut and re-hashed by `verify.py` | +| Table 2 statistics | `paper.txt` lines 359–368 (Q03) | +| Table 3 F1 values | `paper.raw.txt` line 846 (Overall) and 798/803/808/841 | +| the 4 reproduced / 6 failed percentages | `claims.json`, produced by `verify_claims.py` from those table values | +| figure extraction | `figures.json`, produced by `pdfimages -png` from the frozen PDF | +| live title agreement | layer 6 of `verify.py`, re-fetched from the arXiv API at verify time | + +**What this archive does NOT establish:** whether the six unreproduced percentages are +paper errors or a baseline mismatch. The archive establishes that *no denominator present in +the paper's own Table 3 yields them* — that is the measurement. + +--- + +## 5. Verification + +`verify.py` — six layers, 113 checks, exit 0 on a sound archive: + +1. **sources** — every `raw/` file re-hashed against `fetch_manifest.json` +2. **text** — `paper.txt` re-derived from the frozen PDF and re-hashed (extraction is reproducible) +3. **quotes** — all 14 quote records re-cut from `paper.raw.txt` and re-hashed byte-for-byte +4. **tamper** — the controls: a mutated quote *must* fail layer 3, appended bytes *must* fail + layer 1, and a fabricated quote *must* be absent from the paper +5. **claims** — the paper's stated percentages recomputed; the 6 failures pinned +6. **network** — the arXiv API re-fetched live; the title must match the archived title + +Run it: `python3 verify.py` + +--- + +## 6. Files in this artifact + +``` +archive-memgallery-2026-10-21/ + ARCHIVE.md this report + fetch_source.py fetches raw/abs.html, raw/api.xml, raw/paper.pdf; writes fetch_manifest.json + fetch_manifest.json url + http status + bytes + sha256 for each raw source + extract_text.py pdftotext -layout -> paper.txt; writes text_manifest.json + extract_figures.py pdfimages -png -> figures/; writes figures.json + build_quotes.py writes quotes.json (14 records) + verify_claims.py writes claims.json (10 recomputations from Table 3) + diag_denominators.py brute-forces the denominator of each stated percentage + verify.py the verdict: 6 layers, 113 checks + claims.json reproduced / not-reproduced results + quotes.json verbatim quote records with sha256 + figures.json extracted figure hashes + raw/ abs.html, api.xml, paper.pdf (frozen, hashed) + paper.txt pdftotext -layout + paper.raw.txt pdftext -raw + figures/ extracted raster images + probe_meta.py helper: citation_* meta tags + probe_tools.py helper: which PDF readers exist + show2.py show3.py show4.py helper: windowed text dumps +``` diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/build_quotes.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/build_quotes.py new file mode 100644 index 0000000..9908079 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/build_quotes.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +"""quotes.json -- verbatim, hash-pinned quote records from the frozen paper. + +Every record carries: + id, section, kind, quote -- what is claimed + locator -- paper.raw.txt line range, and the page label + source_sha256 -- sha256 of the file the quote was cut from + quote_sha256 -- sha256 of the quote text itself + quote_sha256_method -- how that hash was produced, spelled out + +verify.py re-cuts every quote out of the frozen text and re-hashes it. A quote +that is not byte-for-byte what the paper says fails there. +""" +import hashlib +import json +import os + +BASE = os.path.dirname(os.path.abspath(__file__)) + + +def sha256_bytes(b): + return hashlib.sha256(b).hexdigest() + + +def sha256_text(t): + return sha256_bytes(t.encode("utf-8")) + + +def main(): + text_path = os.path.join(BASE, "paper.raw.txt") + raw = open(text_path, "rb").read() + file_sha = sha256_bytes(raw) + lines = raw.decode("utf-8", errors="replace").split("\n") + + # (id, section, kind, locator note, [line indices in paper.raw.txt]) + SPEC = [ + ("Q01", "Abstract", "claim", + "paper.raw.txt lines 20-34", + [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]), + ("Q02", "Abstract", "claim", + "paper.raw.txt lines 34-40", + [34, 35, 36, 37, 38, 39]), + ("Q03", "3.1 Benchmark Construction", "statistic", + "paper.txt lines 359-368 (Table 2)", + None), + ("Q04", "3.3 Evaluation Framework", "definition", + "paper.raw.txt lines 664-678", + [664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678]), + ("Q05", "3.3.1 Memory Extraction & Adaptation", "definition", + "paper.raw.txt lines 684-692", + [684, 685, 686, 687, 688, 689, 690, 691, 692]), + ("Q06", "4.2 Main Results (RQ1)", "finding", + "paper.raw.txt lines 781-789", + [781, 782, 783, 784, 785, 786, 787, 788, 789]), + ("Q07", "4.2 Main Results (RQ1)", "finding", + "paper.raw.txt lines 850-859", + [850, 851, 852, 853, 854, 855, 856, 857, 858, 859]), + ("Q08", "4.2 Main Results (RQ2)", "finding", + "paper.raw.txt lines 865-878", + [865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878]), + ("Q09", "4.2 Main Results (RQ3-1)", "finding", + "paper.raw.txt lines 887-897", + [887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897]), + ("Q10", "4.2 Main Results (RQ3-2)", "finding", + "paper.raw.txt lines 948-966", + [948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, + 962, 963, 964, 965, 966]), + ("Q11", "4.3 Efficiency Analysis (RQ4)", "finding", + "paper.raw.txt lines 969-978", + [969, 970, 971, 972, 973, 974, 975, 976, 977]), + ("Q12", "4.4 Retriever Analysis (RQ5)", "finding", + "paper.raw.txt lines 1001-1017", + [1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, + 1013, 1014, 1015, 1016]), + ("Q13", "5 Conclusion", "finding", + "paper.raw.txt lines 1019-1029", + [1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028]), + ("Q14", "Limitations", "limitation", + "paper.raw.txt lines 1031-1042", + [1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041]), + ] + + # Table 2 is two-column in -layout mode; quote it from paper.txt instead. + table2 = ("Table 2: Statistics of the Mem-Gallery dataset. | Mem-Gallery | Aspect | " + "Statistics | Conversation Data | Sessions | 240 | Dialogue Rounds | 3,962 | " + "Included Images | 1,003 | Evaluation Data | QA Pairs with Annotated Clues | 1,711 | " + "Included Images | 487") + + records = [] + for qid, section, kind, locator, idxs in SPEC: + if idxs is None: + quote = table2 + src = os.path.join(BASE, "paper.txt") + src_sha = sha256_bytes(open(src, "rb").read()) + else: + quote = "\n".join(lines[i].rstrip() for i in idxs if i < len(lines)) + src = text_path + src_sha = file_sha + records.append({ + "id": qid, + "section": section, + "kind": kind, + "quote": quote, + "locator": locator, + "source_file": os.path.relpath(src, BASE), + "source_sha256": src_sha, + "quote_sha256": sha256_text(quote), + }) + + out = { + "paper": { + "arxiv_id": "2601.03515", + "version": "v1", + "title": "Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents", + "authors": ["Yuanchen Bei", "Tianxin Wei", "Xuying Ning", "Yanjun Zhao", + "Zhining Liu", "Xiao Lin", "Yada Zhu", "Hendrik Hamann", + "Jingrui He", "Hanghang Tong"], + "submitted": "2026-01-07", + "comments": "34 pages, 18 figures", + "url": "https://arxiv.org/abs/2601.03515", + }, + "hash_method": ("quote_sha256 = sha256 of the quote string encoded utf-8; the quote is " + "the exact .rstrip()'d line contents joined with '\\n', with no " + "re-wrapping, no hyphen-dehyphenation and no whitespace normalisation"), + "records": records, + } + with open(os.path.join(BASE, "quotes.json"), "w", encoding="utf-8") as fh: + json.dump(out, fh, indent=2) + fh.write("\n") + + print("wrote %d quote records" % len(records)) + for r in records: + print(" %s %-42s %s" % (r["id"], r["section"][:42], r["quote_sha256"][:16])) + + +if __name__ == "__main__": + main() diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/claims.json b/wizard-work/sphinx/archive-memgallery-2026-10-21/claims.json new file mode 100644 index 0000000..d65c261 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/claims.json @@ -0,0 +1,79 @@ +{ + "checks": [ + { + "claim": "MuRAG overall F1 gain over best textual (paper: 11.85%)", + "recomputed": 11.8497, + "paper_states": 11.85, + "tol": 0.01, + "ok": true + }, + { + "claim": "MuRAG FR gain over best textual (paper: 7.69%)", + "recomputed": 14.9009, + "paper_states": 7.69, + "tol": 0.01, + "ok": false + }, + { + "claim": "MuRAG VS gain over best textual (paper: 12.29%)", + "recomputed": 16.1486, + "paper_states": 12.29, + "tol": 0.01, + "ok": false + }, + { + "claim": "MuRAG TTL gain over best textual (paper: 29.06%)", + "recomputed": 25.2988, + "paper_states": 29.06, + "tol": 0.01, + "ok": false + }, + { + "claim": "MuRAG overall F1 gain over NGM (paper: 4.11%)", + "recomputed": 4.11, + "paper_states": 4.11, + "tol": 0.01, + "ok": true + }, + { + "claim": "MuRAG overall F1 gain over AUGUSTUS (paper: 5.39%)", + "recomputed": 5.3858, + "paper_states": 5.39, + "tol": 0.01, + "ok": true + }, + { + "claim": "Full(MM) vs Full(Text) overall F1 deficit (paper: 8.08%)", + "recomputed": 7.4759, + "paper_states": 8.08, + "tol": 0.01, + "ok": false + }, + { + "claim": "Full(MM) vs MuRAG overall F1 deficit (paper: 51.85%)", + "recomputed": 51.8519, + "paper_states": 51.85, + "tol": 0.01, + "ok": true + }, + { + "claim": "A-Mem refusal F1 deficit vs FIFO (paper: 7.22%)", + "recomputed": 3.1625, + "paper_states": 7.22, + "tol": 0.01, + "ok": false + }, + { + "claim": "MuRAG refusal F1 deficit vs FIFO (paper: 5.82%)", + "recomputed": 1.7013, + "paper_states": 5.82, + "tol": 0.01, + "ok": false + } + ], + "n": 10, + "failed": 6, + "best_textual_model": "A-Mem", + "best_textual_overall_f1": 0.6228, + "source_row": "Table 3 Overall F1 (paper.raw.txt line 846)" +} diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/diag_denominators.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/diag_denominators.py new file mode 100644 index 0000000..a2a9127 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/diag_denominators.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""diag_denominators.py -- find which baseline each paper percentage actually uses. + +The paper states percentages; the table holds F1. If the stated percentage does +not come out of the best-textual baseline, this brute-forces every model as the +denominator so the archive reports the real one instead of guessing. +""" +import itertools +import json +import os +import sys + +BASE = os.path.dirname(os.path.abspath(__file__)) +d = json.load(open(os.path.join(BASE, "claims.json"), encoding="utf-8")) + +# Rebuild the numbers from the same source verify_claims.py uses. +sys.path.insert(0, BASE) +import verify_claims as vc # noqa: E402 + + +def pct(a, b): + return (a - b) / b * 100.0 + + +TARGETS = [ + ("FR", "MuRAG", 7.69), ("VS", "MuRAG", 12.29), ("TTL", "MuRAG", 29.06), +] + +print("== which denominator reproduces each stated task gain? ==") +for task, num_model, target in TARGETS: + row = vc.TASK_F1[task] + num = row[num_model] + hits = [] + for m, v in row.items(): + got = pct(num, v) + if abs(got - target) <= 0.02: + hits.append((m, round(got, 3))) + print("%-4s stated=%6.2f%% numerator=%s(%.4f) matching denominators=%s" % + (task, target, num_model, num, hits or "NONE")) + + # also show every candidate, so 'none' is visible rather than assumed + print(" all: " + ", ".join("%s=%+.2f%%" % (m, pct(num, v)) for m, v in row.items())) + +print() +print("== AR: is 7.22 / 5.82 relative or absolute? ==") +fifo = vc.AR_F1["FIFO"] +for m in ("A-Mem", "MuRAG"): + rel = -pct(vc.AR_F1[m], fifo) + ab = (fifo - vc.AR_F1[m]) * 100.0 + print(" %s vs FIFO: relative=%6.2f%% absolute_points=%6.2f F1=%.4f" % + (m, rel, ab, vc.AR_F1[m])) + +print() +print("== Full(MM) 8.08: relative vs absolute? ==") +fm, ft = vc.OVERALL_F1["Full (MM)"], vc.OVERALL_F1["Full (Text)"] +print(" vs Full(Text): relative=%6.2f%% absolute_points=%6.2f" % (-pct(fm, ft), (ft - fm) * 100.0)) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/extract_figures.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/extract_figures.py new file mode 100644 index 0000000..af6cf68 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/extract_figures.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +"""extract_figures.py -- pull the raster figures out of the frozen PDF and hash them. + +Multimodal archive, so the images are part of the record, not an afterthought. +Figures are extracted as PNG (lossless) and each gets a sha256 in figures.json. +""" +import hashlib +import json +import os +import re +import subprocess + +BASE = os.path.dirname(os.path.abspath(__file__)) +PDF = os.path.join(BASE, "raw", "paper.pdf") +OUT = os.path.join(BASE, "figures") + + +def main(): + os.makedirs(OUT, exist_ok=True) + if not os.path.exists(PDF): + raise SystemExit("missing raw/paper.pdf -- run fetch_source.py first") + prefix = os.path.join(BASE, "_figtmp") + r = subprocess.run(["pdfimages", "-png", PDF, prefix], + capture_output=True, text=True, timeout=600) + if r.returncode != 0: + raise SystemExit("pdfimages rc=%d: %s" % (r.returncode, r.stderr.strip())) + + produced = sorted(f for f in os.listdir(BASE) if f.startswith("_figtmp")) + records = [] + for name in produced: + src = os.path.join(BASE, name) + # pdfimages writes "-NNN.ext"; strip the prefix, keep a sane name + base = name[len("_figtmp"):].lstrip("-") + dest = os.path.join(OUT, base) + os.replace(src, dest) + raw = open(dest, "rb").read() + records.append({"file": "figures/" + base, "bytes": len(raw), + "sha256": hashlib.sha256(raw).hexdigest()}) + + # which page each figure landed on, for citation + listing = subprocess.run(["pdfimages", "-list", PDF], + capture_output=True, text=True, timeout=300).stdout + pages = {} + for line in listing.split("\n")[2:]: + parts = line.split() + if len(parts) > 1 and parts[0].isdigit(): + pages.setdefault(int(parts[0]), 0) + pages[int(parts[0])] += 1 + + payload = { + "source_pdf_sha256": hashlib.sha256(open(PDF, "rb").read()).hexdigest(), + "extractor": "pdfimages -png", + "n_images_in_pdf": len(produced), + "images_per_page": {str(k): v for k, v in sorted(pages.items())}, + "figures": records, + } + with open(os.path.join(BASE, "figures.json"), "w", encoding="utf-8") as fh: + json.dump(payload, fh, indent=2) + fh.write("\n") + print("extracted %d images -> figures/" % len(records)) + for r in records[:6]: + print(" %-28s %7d %s" % (os.path.basename(r["file"]), r["bytes"], r["sha256"][:16])) + if len(records) > 6: + print(" ... and %d more" % (len(records) - 6)) + + +if __name__ == "__main__": + main() diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/extract_text.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/extract_text.py new file mode 100644 index 0000000..2d4439c --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/extract_text.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""extract_text.py -- pdftotext the frozen PDF, hash the text, pull all authors.""" +import hashlib +import html +import os +import re +import subprocess + +BASE = os.path.dirname(os.path.abspath(__file__)) +PDF = os.path.join(BASE, "raw", "paper.pdf") +TXT = os.path.join(BASE, "paper.txt") + + +def main(): + if not os.path.exists(PDF): + raise SystemExit("missing raw/paper.pdf -- run fetch_source.py first") + r = subprocess.run(["pdftotext", "-layout", PDF, TXT], + capture_output=True, text=True, timeout=300) + if r.returncode != 0: + raise SystemExit("pdftotext rc=%d: %s" % (r.returncode, r.stderr.strip())) + raw = open(TXT, "rb").read() + print("paper.txt bytes=%d sha256=%s" % (len(raw), hashlib.sha256(raw).hexdigest())) + + t = raw.decode("utf-8", errors="replace") + print("chars=%d lines=%d" % (len(t), t.count("\n"))) + + # authors from the arXiv abs page meta tags (the PDF byline is layout-scrambled) + a = open(os.path.join(BASE, "raw", "abs.html"), encoding="utf-8", errors="replace").read() + authors = re.findall(r' 1 and parts[0].isdigit(): + pages[int(parts[0])] = pages.get(int(parts[0]), 0) + 1 + +payload = { + "source_pdf_sha256": hashlib.sha256(open(PDF, "rb").read()).hexdigest(), + "extractor": "pdfimages -png", + "n_images_in_pdf": len(records), + "images_per_page": {str(k): v for k, v in sorted(pages.items())}, + "figures": records, +} +with open(os.path.join(BASE, "figures.json"), "w", encoding="utf-8") as fh: + json.dump(payload, fh, indent=2) + fh.write("\n") +print("figures.json: %d records" % len(records)) +print("largest:") +for r in sorted(records, key=lambda x: -x["bytes"])[:5]: + print(" %-14s %8d %s" % (os.path.basename(r["file"]), r["bytes"], r["sha256"][:16])) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/paper.raw.txt b/wizard-work/sphinx/archive-memgallery-2026-10-21/paper.raw.txt new file mode 100644 index 0000000..3e62f8f --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/paper.raw.txt @@ -0,0 +1,3522 @@ +Mem-Gallery: Benchmarking Multimodal Long-Term Conversational +Memory for MLLM Agents +Yuanchen Bei1 +, Tianxin Wei1 +, Xuying Ning1 +, Yanjun Zhao1 +, Zhining Liu1 +, +Xiao Lin1 +, Yada Zhu2 +, Hendrik Hamann3,4 +, Jingrui He1 +, Hanghang Tong1 +1 +University of Illinois Urbana-Champaign 2 +MIT-IBM Watson AI Lab, IBM Research +3 +Stony Brook University 4 +Brookhaven National Laboratory +Abstract +Long-term memory is a critical capability for +multimodal large language model (MLLM) +agents, particularly in conversational settings +where information accumulates and evolves +over time. However, existing benchmarks ei- +ther evaluate multi-session memory in text- +only conversations or assess multimodal un- +derstanding within localized contexts, failing +to evaluate how multimodal memory is pre- +served, organized, and evolved across long- +term conversational trajectories. Thus, we intro- +duce Mem-Gallery1 +, a new benchmark for eval- +uating multimodal long-term conversational +memory in MLLM agents. Mem-Gallery fea- +tures high-quality multi-session conversations +grounded in both visual and textual informa- +tion, with long interaction horizons and rich +multimodal dependencies. Building on this +dataset, we propose a systematic evaluation +framework that assesses key memory capabil- +ities along three functional dimensions: mem- +ory extraction and test-time adaptation, mem- +ory reasoning, and memory knowledge man- +agement. Extensive benchmarking across thir- +teen memory systems reveals several key find- +ings, highlighting the necessity of explicit mul- +timodal information retention and memory or- +ganization, the persistent limitations in memory +reasoning and knowledge management, as well +as the efficiency bottleneck of current models. +1 Introduction +The rapid progress of Multimodal Large Language +Models (MLLMs) has enabled the development of +agents that can perceive, reason, and interact with +the world through both language and vision (Wu +et al., 2023; Zhang et al., 2024a). A fundamental +capability for such agents is long-term memory: +store, retrieve, and update information accumulated +over extended interactions (Zhang et al., 2025b). In +particular, multi-session conversations constitute +1 +https://github.com/YuanchenBei/Mem-Gallery +... I met the sweetest Cocker +Spaniel today! … +… +Multimodal Multi-Session +Conversation +Session-1 +2025-03-02 +Session-n +2025-06-22 +Multi-Round Dialogues +That’s beautiful. They’re +incredibly empathetic dogs. … +…His name is Milo. I think I’m +falling in love with spaniels… +… Once someone meets a +Cocker Spaniel, it’s hard not +to be enchanted! +We finally had Luna’s second +session today! … She was more +cooperative this time. … +Typical Springer energy! … +Familiarity often stabilizes +behavioral responses. … +I want to merge Milo’s video +too. It adds emotional +expression variety. … +After Multi-Session +Conversations… +… +Multi-Round Dialogues +Smart idea! Cocker data will +enrich the affective +dimension of your study. +Example Topics: +Dog, Behavior, +Academic, Life +Multi-Dimension Evaluation +Memory Extraction & Adaptation +Factual Retrieval Visual-centric Search Test-Time Learning +Memory Knowledge Management +Answer Refusal +Conflict Detection +Knowledge Resolution +Visual-centric Reasoning +Temporal Reasoning +Memory Reasoning +Multi-entity Reasoning +Attached Image +Attached Image +Attached Image +Attached Image +What breed is the dog +in the attached image? +Cocker Spaniel: +Others: +… +Conflict with the memory or not: +Sussex Spaniel has white-and- +liver coat pattern and alert eyes? +... Sussex spaniel… +… teddy bear-short fur… +… +After Lena’s correction, which +dog breed does her sister +actually like? +My sister loves Standard Poodles. +I got it wrong earlier. Her favorite +breed is as the image. … +… the Standard Schnauzer … +What is the name of Lena's +friend Amy's cat? +Amy has a Cairn Terrier… she +told me she named her Cairn +Terrier ‘Pebble’… … +Not mentioned +What candidate names +have been listed when +naming Lena's dog? +… Coral... Sunny… or Lumi … +Find the two images related +to Lucy‘s pet. Return the +image_id(s). +When did Lena and Lumi meet +the Papillon at the park? +Date: 2024-09-28 +I took Lumi to the riverside park +today. we met … little dog. +The dog in the picture more +similar to who’s pet? +Lena’s Amy’s +… … +How did Luna and Rocky respond +differently when a clipboard was +dropped during the lab session? +… When … a clipboard, he flinched +but then wagged his tail and … +… Luna, on the other hand, barked +once and … +Timeline +… … +… +… +… +… +… +Lucy’s Other’s +… +… +… +… +Figure 1: The conceptual illustration of Mem-Gallery. +a primary medium through which agents acquire, +refine, and utilize memory, making conversational +settings a natural and critical testbed for evaluating +long-term memory capabilities (Wu et al., 2025). +Human memory in conversation is inherently +multimodal and evolving (Ardesch et al., 2019; +Luppi et al., 2022). An effective agent therefore re- +quires the ability to not only recall past information +but also integrate visual and textual cues, reason +across events, and update outdated knowledge as +conversations progress (Hu et al., 2025b; Bo et al., +2025). However, endowing MLLM agents with +such long-term multimodal conversational memory +largely remains an open challenge, and its system- +atic evaluation is still underexplored. +Despite growing interest in agentic memory, ex- +arXiv:2601.03515v1 +[cs.CL] +7 +Jan +2026 + isting benchmarks reflect a fundamental mismatch +with the real-world conversational memory for +MLLM agents. Current conversation benchmarks +tend to fall into two disjoint categories. On one +hand, text-only conversational memory bench- +marks evaluate memory over multi-session dia- +logues but discard visual modality (Wu et al., 2025; +Hu et al., 2025a). On the other hand, localized +multimodal context understanding benchmarks +introduce the visual modality but typically focus +on short-horizon understanding within only one +session, failing to assess cross-session information +evolution and management (Liu et al., 2024; Xue +et al., 2025). This limitation makes them unsuit- +able for memory evaluation. Consequently, current +conversational benchmarks still fall short in evalu- +ating how agents organize, maintain, and retrieve +multimodal memory over extended conversational +timelines, where visual and linguistic information +interact dynamically. +To address this gap, we propose Mem-Gallery, +a benchmark designed to evaluate multimodal long- +term conversational memory in MLLM agents sys- +tematically, as described in Figure 1. Mem-Gallery +firstly introduces a new dataset of multi-session +conversations grounded in both images and text, +reflecting daily and domain-specific knowledge. +Each conversation spans a long interaction horizon, +where information is incrementally introduced, ref- +erenced, and updated. Based on this dataset, we +structure evaluation tasks into three key functional +dimensions of memory. These three dimensions +correspond to the core stages of long-term conver- +sational memory in real-world agents: acquiring us- +able memory, reasoning over evolving multimodal +evidence, and regulating memory under dynamic +and potentially inconsistent states. Specifically, +Mem-Gallery evaluates: (1) Memory Extraction +and Adaptation, which includes multimodal fac- +tual retrieval, visual-centric search, and test-time +learning over long multimodal histories. (2) Mem- +ory Reasoning, which evaluates how agents con- +duct reasoning over multimodal memory clues, +including temporal reasoning, visual-centric rea- +soning, and multi-entity reasoning. (3) Memory +Knowledge Management, which examines the +ability to resolve knowledge contradictions, detect +conflicts, and appropriately refuse to answer when +information is missing, outdated, or inconsistent. +Benchmarking across thirteen memory mecha- +nisms reveals several findings. (1) Multimodal +Information Effectiveness: explicitly preserving +visual information in memory is beneficial. (2) +Memory Organization Importance: highlight- +ing the necessity of principled multimodal mem- +ory organization and maintenance. (3) Memory +Reasoning and Knowledge Management Limita- +tions: existing multimodal memory models strug- +gle in reasoning-intensive settings, as well as in +handling knowledge updates and conflicts. (4) Ef- +ficiency Bottleneck: multimodal memory overall +introduces larger storage and retrieval overhead +that may hinder practical deployment. +Our contributions are summarized as follows: +• New Scenario & Dataset: We formulate multi- +modal long-term conversational memory as an +evolving system that spans multiple sessions, +modalities, and memory functions, and build a +customized conversational dataset. +• Evaluation Framework: We propose a new eval- +uation framework that systematically assesses +multimodal long-term conversational memory +across memory extraction & adaptation, reason- +ing, and knowledge management. +• Benchmark Takeaways: Through extensive +benchmarking, we reveal key advantages and +limitations of existing memory designs in multi- +modal long-term conversations, providing action- +able insights for future research. +2 Related Works +2.1 Multi-Round Conversational Benchmark +A number of dialogue benchmarks have been pro- +posed in recent years that can be used to evaluate +memory capabilities, such as LoCoMo (Maharana +et al., 2024), LongMemEval (Wu et al., 2025), and +MemoryAgentBench (Hu et al., 2025a). However, +most of these benchmarks are text-only and do not +provide an evaluation of multimodal capabilities. +Although several localized multimodal dialogue +benchmarks have been introduced recently, like +MMDU (Liu et al., 2024) and MMRC (Xue et al., +2025), they are single-session only and lack the +multi-session conversation structure. Therefore, +they are used to assess multi-round context under- +standing abilities rather than long-term memory. +We compare Mem-Gallery with representative +related works in Table 1. Overall, prior benchmarks +exhibit a structural misalignment with multimodal +long-term memory evaluation, either overlooking +visual information, lacking multi-session structure, +or failing to support the assessment of multimodal + Table 1: Comparison between Mem-Gallery with representative conversational benchmarks. ✓: Satisfies; ✗: Does +not satisfy; ✓ +✗: Text modality only. +Benchmark +Conversational Characteristics Extract.&Adapt. Reasoning Management +A. Round A. Img. Multi-Sess. MM Info. FR VS TTL TR VR MR KR CD AR +DuLeMon (Xu et al., 2022) 8.16 — ✓ ✗ ✓ +✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ +DialogBench (Ou et al., 2024) 7.48 — ✓ ✗ ✓ +✗ ✗ ✗ ✗ ✗ ✓ +✗ ✓ +✗ ✗ ✗ +MemoryBank (Zhong et al., 2024) 3.77 — ✓ ✗ ✓ +✗ ✗ ✗ ✓ +✗ ✗ ✗ ✗ ✗ ✗ +MMDU (Liu et al., 2024) 14.95 3.83 ✗ ✓ ✓ ✗ ✗ ✗ ✓ ✓ ✗ ✗ ✗ +LoCoMo (Maharana et al., 2024) 10.81 3.35 ✓ ✓ ✓ ✗ ✗ ✓ +✗ ✗ ✓ ✗ ✗ ✓ +✗ +LOCCO (Jia et al., 2025) 4.77 — ✓ ✗ ✓ +✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ +LongMemEval (Wu et al., 2025) 5.19 — ✓ ✗ ✓ +✗ ✗ ✗ ✓ +✗ ✗ ✓ +✗ ✓ +✗ ✗ ✓ +✗ +MemoryAgentBench (Hu et al., 2025a) 9.55 — ✓ ✗ ✓ +✗ ✗ ✓ +✗ ✓ +✗ ✗ ✓ +✗ ✓ +✗ ✗ ✗ +MMRC (Xue et al., 2025) 12.90 2.90 ✗ ✓ ✓ ✗ ✗ ✓ ✓ ✓ ✓ +✗ ✗ ✓ +Mem-Gallery (Ours) 16.51 4.18 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ +* A. Round: average user-assistant dialogue round numbers per session. A. Img.: average image numbers per session. Multi-Sess. & MM Info.: whether the dataset +has basic characteristics of multi-session and multimodal information. This property serves as a fundamental prerequisite for multimodal long-term conversational +memory. For the three evaluation dimensions: (i) Memory extraction & adaptation includes factual retrieval (FR), visual-centric search (VS), and test-time learning +(TTL) subtasks. (ii) Memory reasoning includes temporal reasoning (TR), visual-centric reasoning (VR), and multi-entity reasoning (MR) subtasks. (iii) Memory +knowledge management includes knowledge resolution (KR), conflict detection (CD), and answer refusal (AR) subtasks. +memory functionalities. Among them, LoCoMo +is one of the few that incorporates visual infor- +mation and a multi-session structure. However, it +supports a very restricted multimodal memory func- +tionality evaluation, which is thus always used for +text-only memory evaluation (Xu et al., 2025; Fang +et al., 2025). Furthermore, Figure 2 shows that in- +corporating visual information in LoCoMo yields +marginal or inconsistent gains, indicating that its +evaluation tasks can largely be solved without vi- +sual clues and thus lack sufficient capacity to assess +multimodal long-term memory. This highlights the +need for a new benchmark that systematically in- +tegrates task-critical multimodal information into +multi-session conversations, enabling a comprehen- +sive evaluation of multimodal memory capabilities. +2.2 Long-Term Agent Memory +Real-world tasks typically require agents to inter- +act with their environments in a multi-round and +dynamic manner, e.g., multi-round dialogues in +conversational agents, making long-term memory +an important capability for agents (Zhang et al., +2025b; Wei et al., 2025). Prior works have ex- +plored different aspects of memory design for such +agents. For example, Generative agents (Park et al., +2023) introduced the concept of memory flow for +social event simulation. A-Mem (Xu et al., 2025) +and MemoryOS (Kang et al., 2025) designed agen- +tic memory construction and maintenance mecha- +nisms. However, existing methods primarily focus +on the textual modality, while real-world mem- +ory often requires the joint integration of mul- +timodal information. Consequently, multimodal +long-term memory has recently garnered growing +interest, such as ViLoMem (Bo et al., 2025) and +Full Memory MemGPT A-Mem +0.06 +0.12 +0.18 +0.24 +0.30 +0.36 +F1 +-0.63% +-2.11% ++1.81% +LoCoMo +Full Memory MemGPT A-Mem +0.30 +0.36 +0.42 +0.48 +0.54 +0.60 +F1 ++5.36% ++15.42% ++22.47% +Mem-Gallery (Ours) +w/o Visual Info. with Visual Info. +Figure 2: Effectiveness analysis of visual information +with representative memory models. Compared with +LoCoMo (Maharana et al., 2024), visual information +plays a much more critical role in Mem-Gallery. +M3-Agent (Long et al., 2025). Although recent +M3-Bench (Long et al., 2025) has taken a step +toward evaluating multimodal long-term memory +with long-video QA, its setup differs fundamen- +tally from conversational memory with multi-round +interactions. Key challenges to long-term conver- +sational memory, such as multimodal information +being incrementally introduced, referenced, and +regulated across sessions for personalized assis- +tants, remain largely underexplored. +3 Mem-Gallery Benchmark +As shown in Figure 3, we describe Mem-Gallery +from three aspects: (1) the benchmark construction, +(2) the unified conversational environment, and (3) +the evaluation framework and task taxonomies. +3.1 Benchmark Construction +The statistics of the new dataset can be found in +Table 2. Detailed dataset construction and statistics +can be found in Appendix A.2. +3.1.1 Conversation Data +To support realistic long-term memory evaluation, +the conversation data is organized as coherent + Data Sourcing +(1) Base Data Curation +(2) Background Generation +Benchmark Construction +Conversational +Environment for Memory +Evaluation Framework +Role Profile +New Story +Creation +Topics +Relevant Topic +Clustering +Conv. Generation (with +New-Created Story) +Conv. Organization (with +Clustered Dialogs) +MLLM-based +Creator +(1) LLM Checking & Editing +(2) Human Checking & Editing +QA Generation +(LLM) +QA Generation +(Human) +Conversations +Evaluation +Taxonomy +Multi-Session Conversation +Average 198.1 Rounds Per Conversation +Average 12.0 Sessions Per Conversation +Multi-Session +Multi-Modal +… +Multi-Round +… +… +10+ Conversation +Memory Agents +Memory Retrieval & Answer +Generation for Evaluation Tasks +Performance +Metrics +Retrieval +Metrics +Efficiency +Metrics +Benchmark Evaluation +Analysis +Comprehensive Evaluation +Data Synthesis Quality Assurance +Evaluation Data Generation +Conversation Generation +LLM +Checking +Human +Checking +Data Synthesis Quality Assurance +… +Stored Memory +MLLM Backbone Memory +Organization and +Maintenance +F1, EM, +BLEU-1, +LLM-Judge +Recall, +Precision, +Hit +Organization +& Retrieval +Time +Memory Extraction & Adaptation +Memory Reasoning +Memory Knowledge Management +Knowledge Resolution Conflict Detection Answer Refusal +Temporal Reasoning Visual-centric Reasoning Multi-entity Reasoning +Factual Retrieval Visual-centric Search Test-Time Learning +Evaluation Taxonomy +Figure 3: The overall pipeline of our proposed Mem-Gallery. +Table 2: Statistics of the Mem-Gallery dataset. +Mem-Gallery Aspect Statistics +Conversation Data +Sessions 240 +Dialogue Rounds 3,962 +Included Images 1,003 +Evaluation Data +QA Pairs with Annotated Clues 1,711 +Included Images 487 +multi-session interactions, with consistent user per- +sonas and tightly coupled visual & textual content. +Data Sourcing. We first curate base materi- +als from open-source resources, including images +and parts of the textual content (details in Ap- +pendix A.2.2). Specifically, we prioritize pub- +licly available data that cover diverse everyday and +domain-specific scenarios, with sufficient visual de- +tail and semantic richness to support long-horizon +multimodal grounding, rather than isolated or short- +context understanding. On top of the curated base +data, we further generate structured conversational +backgrounds, such as user role profiles and conver- +sation topics. These backgrounds serve as control- +lable anchors for conversation synthesis, ensuring +topic diversity and long-range coherence. +Data Synthesis. Based on the sourced mate- +rials, we synthesize multi-session conversations +through two complementary strategies, each pro- +ducing valid multi-session conversations that are +later unified into the final dataset. On one hand, +we adopt conversation generation with newly cre- +ated stories as conversation backgrounds, where +human annotators design the story outline and +inter-session transition logic. Advanced LLMs gen- +erate the text-part multi-session dialogues condi- +tioned on these specifications. Annotators then +insert appropriate images at suitable positions to +ensure multimodal dependency. On the other hand, +we perform conversation organization via topic- +based clustering, where existing single-session mul- +timodal dialogues in MMRC (Xue et al., 2025) are +grouped into candidate multi-session conversations. +Specifically, LLMs are used to extract representa- +tive topic keywords from single-session dialogues, +followed by clustering under constraints on topical +relevance, fluency, and length. The clustered con- +versations are then reordered and refined to form +coherent long-term interaction sequences. Details +and the example can be found in Appendix A.2.3. +Quality Assurance. We apply a two-stage qual- +ity assurance process. In the first stage, advanced +LLMs automatically check and revise conversa- +tions for coherence, fluency, and factual consis- +tency. In the second stage, human annotators care- +fully review each conversation and further refine +the content through manual editing. This ensures +the final conversation data maintains high multi- +modal quality and realistic conversation dynamics. +3.1.2 Evaluation Data +Based on the curated conversations, we systemat- +ically generate evaluation data. It consists of QA +pairs based on a well-predefined evaluation taxon- +omy (details in Section 3.3) for each conversation. +Data Synthesis. Evaluation QA pairs are also +constructed through two complementary ways, +which jointly contribute to the final evaluation set. + On one hand, LLMs are prompted with selected +conversation histories and task descriptions to gen- +erate QA candidates. On the other hand, human an- +notators construct QA pairs by reviewing each con- +versation and designing targeted questions. These +two ways jointly ensure both coverage, difficulty, +and diversity of evaluation instances. In addition, +for each QA pair, we explicitly annotate evidence +clues for the correct answer that specify dialogue +turns in which the relevant information is refer- +enced for the answer. These annotated clues facil- +itate fine-grained analysis of memory behaviors, +e.g., retrieval details (analyzed in Appendix A.6.2), +beyond final task performance. Data synthesis de- +tails can be found in Appendix A.2.4. +Quality Assurance. Generated QA pairs also +undergo a two-stage verification. The same as con- +versation generation, LLMs are first used to check +answer correctness and question clarity. This veri- +fication step is followed by careful human revision. +3.2 Conversational Environment for Memory +Following previous works (Maharana et al., 2024), +memory models are evaluated based on the input +conversation. In our setting, memory agents must +go beyond storing textual information in existing +benchmarks and store and associate visual content +with textual content. For textual memory, image +captions are provided to preserve the visual content. +Conversation Structure. As shown in Figure 8, +each conversation spans multiple sessions with tem- +poral gaps. Within each session, agents engage in +multi-round multimodal dialogues, while across +sessions, information may be updated or contra- +dicted. This design prevents reliance on short-term +context and explicitly requires memory to persist +and evolve across long-term conversational bound- +aries. Visual content in the conversation is not +limited to single-round perception but may be ref- +erenced and integrated with textual context across +sessions. Consequently, agents must integrate mul- +timodal clues distributed over extended timelines, +rather than treating images as isolated observations. +Benchmarking Memory Methods. We bench- +mark thirteen representative memory models under +a unified setting. As conversations progress, agents +accumulate an expanding memory. To ensure a fair +and controlled comparison across diverse designs, +all evaluated models follow a unified memory ac- +cumulation granularity and protocol (Kang et al., +2025), where information is incrementally stored +along with the conversational timeline. Memory +Total: +1,711 QA +Pairs +Memory +Extract. +& Adapt. +50.4% +Factual Retrieval +12.8% +Visual-centric +Search +17.9% +Test-Time Learning +19.7% +Memory +Reasoning +29.4% +Multi-entity +Reasoning +12.0% +Visual-centric +Reasoning +10.2% +Temporal +Reasoning +7.2% +Memory +Knowledge +Management +20.2% +Knowledge +Resolution +4.7% +Conflict +Detection +4.7% +Answer +Refusal +10.8% +Figure 4: Taxonomy and distribution of evaluation tasks. +retrieval and answer generation are then performed +based on the accumulated memory. The problem +definition can be found in Appendix A.1. +3.3 Evaluation Framework +As shown in Figure 4, we design three task fam- +ilies to systematically evaluate multimodal long- +term memory as an evolving capability in agentic +systems over extended conversations. Specifically, +memory extraction and adaptation assess whether +agents can retrieve relevant multimodal informa- +tion and adapt it as memory accumulates. Memory +reasoning examines whether agents can integrate +and reason over memory, accounting for temporal +dependencies, visual evidence, and multiple en- +tities. Memory knowledge management focuses +on how agents regulate long-term memory under +dynamic states, like handling inconsistencies, re- +solving conflicts, and refusing to answer when in- +formation is outdated, contradictory, or incomplete. +3.3.1 Memory Extraction & Adaptation +At a fundamental level, memory agents must pos- +sess the capability of memory extraction and adap- +tation, ensuring that stored information can be ef- +fectively utilized (Hu et al., 2025a). Based on the +properties, we design three subtask categories. (1) +Factual Retrieval: evaluate the ability to accurately +recall factual details (e.g., user preferences and past +events) from multimodal interaction histories. (2) +Visual-centric Search: evaluate whether the model +can identify or retrieve specific visual instances +from memory (e.g., shared images). (3) Test-Time +Learning: measure the ability to adapt its memory +to unseen multimodal examples at inference time. + 3.3.2 Memory Reasoning +In addition to memory extraction, agents should be +equipped with reasoning capabilities over the mem- +ory to address complex tasks (Ke et al., 2025). We +design three subtasks accordingly. (1) Temporal +Reasoning: assess whether the model can synthe- +size and reason over temporally dependent ques- +tions from multimodal memory. (2) Visual-centric +Reasoning: test the model’s capacity to retrieve +and utilize visual information as cues for reasoning +on multimodal memories. (3) Multi-entity Reason- +ing: reasoning with multiple entities from memory, +where each can be textual or visual. +3.3.3 Memory Knowledge Management +Unlike task-oriented scenarios such as web naviga- +tion, conversations are inherently more open-ended. +Users may update previous information or provide +incorrect details inadvertently during dialogues (Hu +et al., 2025a). To evaluate these challenges, we de- +sign the following subtasks. (1) Knowledge Resolu- +tion: Examine the ability to correctly update stored +knowledge when new, contradictory information +appears in the dialogue, maintaining consistency +over time. (2) Conflict Detection: Test whether the +model can detect conflicts between newly observed +information and existing memory. (3) Answer Re- +fusal: Assess the model’s capability to abstain or +refuse to answer when the requested information is +unsupported by prior memory. +4 Benchmarking Analysis +We conduct extensive experiments on our proposed +Mem-Gallery for the following research questions. +• RQ1: How effective are different multimodal +storage designs? +• RQ2: How important are memory organization +strategies in multimodal memory systems? +• RQ3: What are the strengths and weaknesses of +different memory models across task types? +• RQ4: What are the runtime efficiency character- +istics of different memory approaches? +• RQ5: How does the number of retrieved memory +entries affect the overall performance? +4.1 Evaluation Setup +4.1.1 Model Implementation +MLLM Backbones. To ensure a broad coverage +of model capacities, we use representative open- +source MLLMs at two model scales, Qwen2.5- +VL-3B-Instruct and Qwen2.5-VL-7B-Instruct (Bai +et al., 2025), as well as representative closed-source +MLLMs, namely GPT-4.1-Nano (Achiam et al., +2023) and Gemini-2.5-Flash-Lite (Comanici et al., +2025), as backbone models. We adopt Qwen2.5- +VL-7B-Instruct as our default MLLM backbone. +Memory Models. We comprehensively include +thirteen representative approaches, including eight +textual memory methods and five multimodal mem- +ory methods. Specifically, the textual mem- +ory includes Full Memory (Text), First-in-first- +out (FIFO), NaiveRAG, Generative Agents (Park +et al., 2023), Reflexion (Shinn et al., 2023), +MemGPT (Packer et al., 2023), A-Mem (Xu et al., +2025), and MemoryOS (Kang et al., 2025). The +multimodal memory includes Full Memory (Mul- +timodal), MuRAG (Chen et al., 2022), Universal- +RAG (Yeo et al., 2025), NGM (Fisher, 2025), and +AUGUSTUS (Jain et al., 2025). Details of these +models are provided in Appendix A.4. For methods +that require a top-K retriever, we adopt a default +retrieval size of K=10. To enable a fair comparison +with multimodal memory systems, we provide tex- +tual memory with high-quality image captions gen- +erated by GPT-5.1. The detailed benchmark setup +for fair comparison is shown in Appendix A.5. +4.1.2 Evaluation Metrics +Following existing works, we evaluate memory +performance using F1, BLEU-1, EM, and LLM-as- +a-Judge metrics. For LLM-as-a-Judge, we adopt +Qwen-2.5-72B-Instruct (Bai et al., 2025) as the +judging model. Note that the conflict detection task +explicitly requires models to output either “Yes” or +“No”. Since MLLMs possess instruction-following +capability, the values of all metrics are identical for +this task. For retrieval analysis, we adopt the widely +used Recall, Precision, and Hit as the metrics. The +details can be found in Appendix A.5.4. +4.2 Main Results (RQ1-RQ3) +Table 3 illustrates the main benchmarking results. +We can have the following observations. +RQ1: Explicit multimodal memory preserva- +tion is critical, but increased architectural com- +plexity does not necessarily bring better perfor- +mance. Although with high-quality image cap- +tions, textual memory baselines still generally ex- +hibit a performance gap compared to multimodal +approaches, particularly in memory extraction and +adaptation. MuRAG, a simple multimodal method, +can achieve the best overall performance among + Table 3: Main evaluation results on our Mem-Gallery based on Qwen-2.5-VL-7B. The best and second-performed +memory model(s) are highlighted with orange and blue backgrounds. Results on Qwen-2.5-VL-3B, GPT-4.1-Nano, +and Gemini-2.5-Flash-Lite can be found in Table 8, Table 9, and Table 10, respectively. +Qwen-2.5-VL-7B Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2376 0.1446 0.5852 0.2424 0.2391 0.5928 0.6072 0.6244 0.2150 0.6724 0.6632 0.6364 0.6162 +BLEU-1 0.1865 0.1000 0.5045 0.1862 0.1903 0.5098 0.5138 0.5354 0.1626 0.5755 0.5658 0.5606 0.5309 +EM 0.0913 0.0457 0.3059 0.0868 0.0913 0.3288 0.2922 0.3470 0.0685 0.3607 0.3470 0.3744 0.3288 +LLM-Judge 0.2626 0.1324 0.7763 0.2945 0.2626 0.8539 0.7808 0.8265 0.2260 0.8790 0.8744 0.8082 0.8082 +VS +F1 0.1992 0.0612 0.7592 0.2970 0.1954 0.6239 0.7681 0.7853 0.1658 0.8818 0.8708 0.8531 0.8499 +BLEU-1 0.1873 0.0549 0.7063 0.2616 0.1840 0.5834 0.6880 0.7181 0.1473 0.8442 0.8343 0.8145 0.7942 +EM 0.1601 0.0392 0.5686 0.2124 0.1569 0.4118 0.5882 0.6046 0.1078 0.6699 0.6699 0.6863 0.6536 +LLM-Judge 0.1961 0.0556 0.7402 0.2958 0.1895 0.5964 0.7369 0.7729 0.1683 0.8856 0.8611 0.8480 0.8399 +TTL +F1 0.4500 0.3351 0.6526 0.4851 0.4486 0.2924 0.6336 0.5484 0.4147 0.8177 0.7824 0.7817 0.7913 +BLEU-1 0.3799 0.2692 0.5835 0.4150 0.3798 0.2295 0.5595 0.4697 0.3477 0.7449 0.7103 0.7168 0.7206 +EM 0.2374 0.1365 0.4718 0.2967 0.2374 0.1009 0.4362 0.3561 0.2107 0.6172 0.6142 0.6024 0.6113 +LLM-Judge 0.7092 0.6677 0.8457 0.7582 0.7033 0.7092 0.7997 0.7715 0.7107 0.9006 0.8501 0.9110 0.8932 +Reasoning +TR +F1 0.2545 0.1549 0.4887 0.2742 0.2553 0.5661 0.5604 0.5497 0.2294 0.5833 0.5460 0.5425 0.5800 +BLEU-1 0.2363 0.1316 0.4587 0.2473 0.2363 0.5326 0.5361 0.5240 0.2065 0.5537 0.5137 0.5143 0.5527 +EM 0.1545 0.0894 0.3496 0.1626 0.1545 0.3496 0.4065 0.3821 0.1382 0.4309 0.4065 0.4146 0.4390 +LLM-Judge 0.2805 0.1463 0.6382 0.3252 0.2764 0.8008 0.6951 0.7195 0.2480 0.7724 0.7520 0.7195 0.7398 +VR +F1 0.2552 0.1207 0.3022 0.1955 0.2594 0.4593 0.4477 0.4280 0.2015 0.4818 0.4879 0.4615 0.3866 +BLEU-1 0.2442 0.1005 0.2873 0.1815 0.2480 0.4459 0.4331 0.4126 0.1912 0.4625 0.4682 0.4428 0.3726 +EM 0.2011 0.0690 0.1954 0.1207 0.2011 0.3851 0.3563 0.3391 0.1609 0.3793 0.3793 0.3678 0.2931 +LLM-Judge 0.3046 0.1408 0.3793 0.2471 0.3046 0.6149 0.5747 0.5805 0.2586 0.6092 0.5977 0.5460 0.4856 +MR +F1 0.2411 0.1745 0.4640 0.2450 0.2428 0.4367 0.5000 0.4490 0.2101 0.5007 0.5013 0.4746 0.4866 +BLEU-1 0.1739 0.1184 0.3543 0.1784 0.1770 0.3347 0.3908 0.3446 0.1429 0.3903 0.3868 0.3635 0.3778 +EM 0.0340 0.0146 0.0874 0.0243 0.0340 0.0631 0.0728 0.0728 0.0194 0.0874 0.0971 0.0728 0.0825 +LLM-Judge 0.2985 0.1602 0.7791 0.3350 0.3058 0.8204 0.8083 0.8204 0.2791 0.8447 0.8422 0.7840 0.8228 +Knowledge +Management +KR +F1 0.2354 0.1697 0.3595 0.2674 0.2354 0.4292 0.4515 0.5031 0.2181 0.4601 0.4336 0.3944 0.3752 +BLEU-1 0.2005 0.1424 0.2986 0.2341 0.2000 0.3741 0.3992 0.4479 0.1883 0.4073 0.3772 0.3444 0.3159 +EM 0.1235 0.0864 0.1605 0.1235 0.1235 0.2099 0.2469 0.2716 0.1235 0.2593 0.2099 0.2099 0.1728 +LLM-Judge 0.3395 0.2469 0.6358 0.3457 0.3395 0.7407 0.6667 0.7593 0.2840 0.7840 0.7222 0.6728 0.6728 +CD +F1 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 +BLEU-1 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 +EM 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 +LLM-Judge 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 +AR +F1 0.9958 1.0000 0.9581 0.9841 0.9958 0.9849 0.9278 0.9845 0.9946 0.9418 0.9473 0.9580 0.9460 +BLEU-1 0.9953 1.0000 0.9575 0.9839 0.9953 0.9844 0.9257 0.9841 0.9946 0.9409 0.9466 0.9574 0.9459 +EM 0.9946 1.0000 0.9565 0.9837 0.9946 0.9837 0.9239 0.9837 0.9946 0.9402 0.9457 0.9565 0.9457 +LLM-Judge 0.9783 0.9837 0.9402 0.9674 0.9783 0.9674 0.9375 0.9674 0.9837 0.9375 0.9429 0.9457 0.9429 +Overall +F1 0.3625 0.2724 0.5974 0.3825 0.3619 0.5282 0.6228 0.6109 0.3354 0.6966 0.6827 0.6691 0.6610 +BLEU-1 0.3279 0.2408 0.5441 0.3422 0.3279 0.4792 0.5629 0.5534 0.2999 0.6432 0.6286 0.6200 0.6069 +EM 0.2519 0.1835 0.4161 0.2613 0.2507 0.3402 0.4296 0.4278 0.2279 0.4985 0.4927 0.4944 0.4757 +LLM-Judge 0.4331 0.3369 0.7241 0.4643 0.4307 0.7306 0.7431 0.7613 0.4129 0.8229 0.8016 0.7861 0.7797 +well-designed textual memory models. Specifically, +it achieves 11.85%, 7.69%, 12.29%, and 29.06% F1 +improvement over the best-performed textual mem- +ory on overall, FR, VS, and TTL tasks, respectively. +Furthermore, MuRAG and UniversalRAG, which +simply preserve multimodal information without +structured memory organizations, achieve stronger +performance than complex multimodal memory +systems. For example, MuRAG outperforms 4.11% +and 5.39% overall F1 improvements over NGM and +AUGUSTUS, respectively. This contrast suggests +that, beyond focusing on the memory architecture +itself, how to effectively preserve multimodal infor- +mation so as to maximize the utility of the memory +architecture remains an open challenge. +RQ2: Visual information faces harsher to- +ken consumption, making principled organi- +zation and maintenance critical. Without the +proper organization, inputting all multimodal infor- +mation into the context leads Full Memory (MM) +to perform even worse than Full Memory (Text) +in both memory extraction and reasoning tasks. +Overall, Full Memory (MM) performs 8.08% and +51.85% worse F1 score than Full Memory (Text) +and MuRAG. As visual information is far more +token-heavy than text (Zhang et al., 2025a), under +the token limit, naïvely accumulating multimodal +content can introduce irrelevant visual noise, crowd +out informative text, and thus hurt performance. +Besides, compared to MuRAG and UniversalRAG, +although existing models like NGM and AUGUS- +TUS organize multimodal information in a struc- +tured manner, they still lack effective strategies for +long-term conversational scenarios. Moreover, ex- +isting multimodal methods still lack agentic main- +tenance strategies like A-Mem and MemoryOS, +which also constrain their performance. +RQ3-1: Existing multimodal memory meth- +ods struggle on memory reasoning tasks. Al- +though multimodal memory methods overall out- +perform textual memory approaches, we observe +that their advantages are not pronounced in scenar- +ios requiring reasoning. This is particularly evident +in multi-entity reasoning and temporal reasoning +tasks. Notably, even in the visual-centric reasoning +task that explicitly relies on visual clues, MemGPT, +a textual memory method, can achieve near-optimal +performance. This suggests that existing multi- + Full (Text) +FIFO +NaiveRAG +Gen. Agent +Reflexion +MemGPT +A-Mem +MemoryOS +Full (MM) +MuRAG +UniversalRAG +NGM +AUGUSTUS +10 1 +101 +103 +Memory +time +(s) Textual Memory +Multimodal Memory +Full (Text) +FIFO +NaiveRAG +Gen. Agent +Reflexion +MemGPT +A-Mem +MemoryOS +Full (MM) +MuRAG +UniversalRAG +NGM +AUGUSTUS +103 +104 +Retrieval +& +generation +time +(s) +Figure 5: Efficiency comparison results on information +memorization time (top) and memory retrieval & answer +generation time (bottom) in seconds with the log scale. +modal memory approaches still primarily focus on +the storage and retrieval of multimodal informa- +tion, yet how to effectively reason over multimodal +contents in memory remains largely unexplored. +RQ3-2: Existing methods show limitations in +scenarios involving information updates or con- +flicts. From the memory knowledge management +results, we derive two key observations. First, there +is a trade-off in refusal behavior. Methods with +weaker memory, e.g., FIFO, tend to exhibit stronger +refusal performance, as they default to refusing +when relevant information cannot be retrieved. In +contrast, methods with stronger capabilities on +memory extraction and reasoning show poorer re- +fusal performance, like A-Mem and MuRAG, with +7.22% and 5.82% worse F1 score than FIFO. This +suggests that future methods need to strike a bal- +ance between retrieving relevant information and +distinguishing outdated and conflicting informa- +tion to build safe and hallucination-free memory +systems. Second, for both knowledge resolution +and conflict detection tasks, neither multimodal nor +textual memory methods achieve satisfactory per- +formance. This indicates that designing memory +systems to support dynamic conflict detection and +memory correction remains an important direction. +Full analysis under different MLLMs and case +studies can be found in Appendix A.6.1 and A.7. +4.3 Efficiency Analysis (RQ4) +Figure 5 presents an efficiency comparison of exist- +ing memory methods. From the results, we can see +that multimodal memory incurs higher overhead +than text-only memory in general. Even a rela- +tively simple multimodal memory like MuRAG can +approach the computational cost of highly sophis- +ticated textual memory systems, such as A-Mem +Table 4: Retrieval metrics on different size K. +Recall@K K=5 K=10 K=15 K=20 +Gen. Agent 0.1707 0.2385 0.2854 0.3153 +NaiveRAG 0.5381 0.6723 0.7420 0.7877 +MuRAG 0.7506 0.8601 0.8990 0.9228 +UniversalRAG 0.7311 0.8411 0.8781 0.8998 +NGM 0.6192 0.7475 0.7892 0.8065 +AUGUSTUS 0.6729 0.7529 0.7785 0.7860 +Precision@K K=5 K=10 K=15 K=20 +Gen. Agent 0.0955 0.0684 0.0544 0.0453 +NaiveRAG 0.2694 0.1757 0.1315 0.1047 +MuRAG 0.3686 0.2220 0.1572 0.1220 +UniversalRAG 0.3691 0.2206 0.1555 0.1204 +NGM 0.3564 0.3457 0.3450 0.3424 +AUGUSTUS 0.3341 0.2488 0.2213 0.2124 +and MemoryOS. This suggests that introducing +multimodal signals fundamentally increases system +complexity, regardless of whether advanced mem- +ory organization strategies are employed. Thus, +while multimodal memory can provide richer con- +textual information, it also introduces considerable +inference-time overhead, which may limit its practi- +cality in long-horizon or real-time agent scenarios. +4.4 Retriever Analysis (RQ5) +Table 4 shows multimodal retrievers like MuRAG +and UniversalRAG achieve substantially higher +clue recall. Though recall improves as K increases, +the expanded retrieval coverage does not con- +sistently translate into better QA performance. +Figure 15 shows multimodal memory models typ- +ically exhibit diminishing or saturated task gains +beyond a moderate K, and may degrade at larger +K. Table 4 explains this discrepancy. While the +Recall of MuRAG and UniversalRAG increases +as K grows, its Precision drops sharply, indicat- +ing the introduction of substantial noise. NGM +and AUGUSTUS exhibit more conservative recall +growth with relatively stable Precision, suggesting +structured organization or relevance filtering is an +effective way to balance coverage and noise. Full +analysis and results are reported in Appendix A.6.2. +5 Conclusion +In this paper, we present Mem-Gallery, a bench- +mark for evaluating multimodal long-term conver- +sational memory. By grounding multi-session con- +versations in tightly coupled visual and textual con- +texts, Mem-Gallery enables systematic assessment +beyond prior text-centric memory or localized mul- +timodal context understanding benchmarks. Exten- +sive evaluation reveals several takeaways. These +findings highlight the need for principled memory +organization, selective retrieval, and robust mainte- +nance for future multimodal memory design. + Limitations +While Mem-Gallery provides a comprehensive +evaluation of multimodal long-term conversational +memory, the following limitations remain. 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Memorybank: Enhancing large +language models with long-term memory. In Pro- +ceedings of the AAAI Conference on Artificial Intelli- +gence, volume 38, pages 19724–19731. + A Appendix +Contents +A.1 Problem Definition . . . . . . . . 13 +A.2 Data Construction Details and +Statistics . . . . . . . . . . . . . . 13 +A.2.1 Data Statistics . . . . . . 13 +A.2.2 Data Source . . . . . . . . 13 +A.2.3 Conversation Data Synthesis 14 +A.2.4 Evaluation Data Synthesis 16 +A.3 Detailed Evaluation Task Descrip- +tions . . . . . . . . . . . . . . . . 16 +A.4 Baseline Details . . . . . . . . . . 18 +A.4.1 Textual Memory with Vi- +sual Caption . . . . . . . 18 +A.4.2 Multimodal Memory . . . 18 +A.5 Benchmark Evaluation Details . . 19 +A.5.1 Experimental Environments 19 +A.5.2 Benchmark Settings . . . 19 +A.5.3 Evaluation Pipeline . . . . 19 +A.5.4 Evaluation Metrics . . . . 20 +A.6 Additional Results and Analysis . 21 +A.6.1 MLLM Backbone Analysis 21 +A.6.2 Memory Retrieval Analysis 24 +A.7 Case Study . . . . . . . . . . . . 26 +A.8 Ethics Statement . . . . . . . . . 28 +A.8.1 Data Privacy . . . . . . . 28 +A.8.2 Annotation . . . . . . . . 28 +A.8.3 Scientific Artifacts . . . . 29 +A.9 Potential Risks . . . . . . . . . . 29 +A.10 Use of AI Assistants . . . . . . . 30 + A.1 Problem Definition +Notations. We consider an MLLM-based agent +interacting with an environment (user) over a se- +quence of time steps t ∈ {1, . . . , T}. Let O = +{o1, o2, . . . , oT } be a stream of multimodal obser- +vations, where each observation entry ot = ⟨vt, st⟩ +may contain visual vt and textual st modalities. +The agentic memory system is defined by the tuple +S = ⟨M, fθ, Φ, R⟩, where: +• Mt = {m1, m2, . . . , mn} is the external mul- +timodal memory, an unbounded set of atomic +units. Each unit mi encapsulates raw assets, +cross-modal descriptions, and a joint latent +embedding ei. +• fθ is a multimodal encoder that projects het- +erogeneous data into a unified d-dimensional +latent space Rd. +• Φ is the update operator that governs the tran- +sition of information between new informa- +tion in O and M, including memory consoli- +dation and eviction. +• R is the retrieval operator used to identify and +surface relevant historical information from +M based on current needs. +Memory Construction and Maintenance. The +memory evolution occurs continuously as the agent +ingests the observation stream O from the conver- +sation. At each time step t, the update operator +Φ consolidates new observations into the existing +memory repository: +Mt+1 = Φ(Mt, ot, πevo), (1) +where πevo represents an autonomous evolution +policy, such as memory add, merge, or delete (Yan +et al., 2025; Xiong et al., 2025). +Memory Retrieval. Generation is invoked at a +specific task timestamp τ. Given a multimodal +user query qτ , the retrieval operator R surfaces a +relevant subset Mret from the current state of the +memory repository: +Mret = {mi ∈ Mτ | rank(sim(fθ(qτ ), fθ(mi))) ≤ K}, +(2) +where sim(·) is a similarity scoring function which +computes the multimodal similarity and K is the +retrieval size. +Memory-Augmented Generation. The final ac- +tion or response yτ is generated by the MLLM +agent by conditioning on the fusion of the working +context Cτ , retrieved external knowledge Mret, +and the specific task query qτ at timestamp τ: +yτ = MLLM(Cτ ⊕ Mret ⊕ qτ ), (3) +where ⊕ denotes the integration of heterogeneous +tokens. This architecture ensures that the agent can +maintain long-term coherence and persona consis- +tency without being limited by the context window +of the underlying model (Zhang et al., 2025b). +A.2 Data Construction Details and Statistics +A.2.1 Data Statistics +Mem-Gallery is a new benchmark featuring: (1) +scenario diversity, with long, multimodal, and +multi-session conversations spanning twenty sce- +narios; (2) non-trivial multimodality, where visual +and textual information are genuinely complemen- +tary and both important; and (3) multi-faceted ca- +pabilities, comprehensively assessing key mem- +ory abilities including extraction, test-time adapta- +tion, reasoning, and knowledge management. The +dataset in Mem-Gallery is constructed across 20 +diverse conversation scenarios, comprising a total +of 240 multi-session dialogues with 3,962 conver- +sational rounds. It incorporates 1,003 input images, +which are naturally grounded within the dialogue +context. The details of each scenario can be found +in Table 5. Based on these dialogues, we further cu- +rate 1,711 human-annotated question–answer pairs +to support systematic evaluation. Among them, +487 questions are explicitly associated with visual +inputs, requiring models to reason over both tex- +tual and visual information. The details can be +found in Table 6. This design ensures broad cov- +erage of multimodal conversational settings while +enabling fine-grained analysis of long-term multi- +modal memory capabilities. +A.2.2 Data Source +Image Data Source. To ensure sufficient eval- +uation difficulty and diversity, we collect fine- +grained image categories as well as images that +are closely related to the manually designed di- +alogue topics as multimodal conversation con- +struction materials. All collected images are se- +lected from sources with permissive knowledge- +sharing licenses. These image resources include + Table 5: Detailed statistics of the new dataset in our Mem-Gallery. Each multi-session conversation scenario +contains multiple related dialogue topics. +Scenarios Sessions Rounds Images in Dialogs QAs Images in Questions +AI, Robotics, Automation, Future Tech 12 185 31 57 8 +Academic, Animal, Pet, Research, Life 14 177 45 77 28 +Architecture, Art, Culture, Exhibition, Technology 14 186 30 74 6 +Astronomy, Physics, Scientific Experiments, Cosmology 11 203 30 54 2 +Baking, Dessert, Daily Life, Skill 15 262 57 111 32 +Dog, Behavior, Research, Academic, Life 11 180 38 63 21 +Education, Career, Research, Lifestyle 12 227 56 106 21 +Entrepreneurship, Blockchain, Economics, Logistics, Nature 10 144 36 90 25 +Fashion, Personal Care, Lifestyle, Shopping 9 168 37 61 13 +Global Travel, Culture, Sightseeing 10 176 66 87 29 +Global Travel, Sustainable Fashion, Design 9 159 53 78 29 +Home, Health, Lifestyle, Product 10 163 36 74 8 +Home, Repair, Maintenance, Cleaning 18 270 43 68 0 +Landscape, Travel, Architecture, Nature 11 173 64 92 38 +Music, Dance, Theater, Performance, Learning 14 220 43 57 1 +Nature, Economics, Programming, Student Life 10 201 78 136 67 +Parenting, Commuting, Hobbies, Travel, Gear 16 221 48 82 14 +Python, Botany, AI, Student Life 12 252 117 205 110 +Real Estate, Home Decor, DIY, Lifestyle 11 196 53 75 11 +Technology, Ethics, Future Society 11 199 42 64 24 +Total Statistics 240 3,962 1,003 1,711 487 +MMDU2 (Liu et al., 2024), CUB-200-20113 (Wah +et al., 2011), Stanford Dogs4 (Khosla et al., 2011), +Oxford flower5 (Nilsback and Zisserman, 2008), +and DeepFashion6 (Liu et al., 2016). After collec- +tion, we manually select appropriate images and +insert them at information-relevant positions within +the generated textual conversations. The details can +be found in Appendix A.2.3. +Single-Session Dialogue Data Source. In +addition to newly created materials, we recon- +struct existing single-session dialogues with mul- +timodal information, sourced from prior dataset +MMRC7 (Xue et al., 2025). Specifically, we first +employ advanced LLMs, namely GPT-5.1 and +Gemini-2.5-Pro, to extract representative keywords +from each single-session dialogue, after which +the higher-quality outputs are manually selected. +Based on the extracted keywords, we further use +LLMs to perform session-level clustering. Once +candidate multi-session dialogues are formed, we +conduct additional human filtering and enhance- +ment to ensure the overall quality and coherence of +the reconstructed multi-session conversations. The +2 +https://github.com/Liuziyu77/MMDU +3 +https://www.vision.caltech.edu/datasets/cub_ +200_2011 +4 +http://vision.stanford.edu/aditya86/ +ImageNetDogs +5 +https://www.robots.ox.ac.uk/~vgg/data/ +flowers/102 +6 +https://mmlab.ie.cuhk.edu.hk/projects/ +DeepFashion.html +7 +https://github.com/haochen-MBZUAI/MMRC +details can be found in Appendix A.2.3. +A.2.3 Conversation Data Synthesis +As shown in Figure 6, our conversation data syn- +thesis follows two main approaches. +First, we generate high-quality conversational +stories with the assistance of human annotators. +Annotators initially create the protagonist’s profile +and overall conversation themes, and then spec- +ify the core content of each session through story +outlines, as well as the transition logic between +sessions. Based on this setup, dialogue generation +proceeds in two stages. LLMs are first used to +generate the textual dialogue content. Specifically, +we employ GPT-5.1 and Gemini-2.5-Pro in parallel +and manually select higher-quality outputs. The +dialogues are then further refined, and appropriate +images are inserted at suitable positions to establish +multimodal dependencies. Each multimodal multi- +session long conversation is constructed following +this process, after which quality assurance and QA +generation are conducted. +Second, we reconstruct existing single-session +dialogue datasets via topic-based clustering. In this +approach, LLMs are first used to extract session- +level topic keywords and perform clustering. Hu- +man annotators then review and refine the clustered +results, and summarize the corresponding user pro- +files and multi-session conversation topics. Since +the resulting clustered long conversations often suf- +fer from limited fluency, weak inter-session transi- +tion logic, and insufficient length, we further refine + User Profile: +{ +"name": "Lena", +“persona_summary”: "Lena is a 21-year-old +university student majoring in Life Sciences. +She has always been fascinated by animals and +spends much of her free time volunteering at +the campus animal care center. ...", +"traits": ["curious", "caring", "enthusiastic"], +“conversation_style”: "Warm and inquisitive, +Lena’s conversations often blend empathy with +scientific curiosity. She expresses genuine +interest in understanding how things work ..." +} +Conversation Topics +Academic, Animal, +Pet, Research, Life +… +User … +Assistant +… +Session-1 +… +User … +Assistant +… +Session-n +(a) Conversation Generation with Newly Created Story +Background +Creation +Story Outline & Inter-Session Transition Logic for +Each Conversation +Sessions +Thinking of +adopting a dog +S-1 +Adopting and +naming a dog +AI for animal +behavior +Online animal +behavior seminar +Classmate’s dog & +discuss breeds +Road Travel +with the dog +S-2 S-3 +… +S-4 S-k +… +S-n +… +Conversation Generation with LLMs and +Image Insert by Human Annotators +… +Single-Session Dialog +Clustering +Refinement and +Summarization +User Profile: +{ +"name": "Julian", +“persona_summary”: "Julian is a 19-year- +old first-year undergraduate student in +architecture, fascinated by how historical +spaces and modern technology can shape +human emotion and memory.", +“traits”: [“inquisitive”, “introspective”, +"creative", "methodical"], +“conversation_style”: "Thoughtful and +inquisitive, often exploring concepts ..." +} +Conversation Topics +Architecture, Art, +Culture, Exhibition, +Technology +… +User … +Assistant +… +Session-1 +… +User … +Assistant +… +Session-n +… +Reorder, Refinement, and Include more Sessions +by Human Annotators +Quality Assurance & +QA Generation +Quality Assurance & +QA Generation +(b) Conversation Generation with Topic-based Clustering +Figure 6: Illustration of the generation for each conversation, including two ways. +the dialogue content to ensure coherence and rea- +sonable session transitions. To address insufficient +conversation length, we additionally extend these +clustered conversations by introducing more ses- +sions, which are generated by LLMs and then man- +ually verified and augmented with images, while +preserving logical continuity across sessions. +The distribution of image numbers per ses- +sion in Mem-Gallery is described in Figure 7. +From the distribution, we observe that, beyond +the importance and high quality of visual infor- +mation in Mem-Gallery as compared in Figure 2, +Mem-Gallery significantly reduces the proportion +of sessions with no images or only a single image +compared to LoCoMo (Maharana et al., 2024). The +overall distribution shifts rightward, with most ses- +sions containing at least two images, and the num- +ber of sessions with multiple images (e.g., seven +or more images) is substantially increased. In the +most extreme cases, a single session contains up to +                 +1XPEHURI,PDJHVSHU6HVVLRQ + + + + + + + + +3HUFHQWDJHRI6HVVL +R +QV  +/R&R0R +0HP*DOOHU\ +Figure 7: Image number distribution of Mem-Gallery +per conversation session compared with LoCoMo. +17 images. This design increases both the density +of visual information in conversations and the over- +all difficulty of the dialogue tasks. The conversa- +tion structure of Mem-Gallery and the information +feed way for memory agents are shown in Figure 8. + Table 6: Evaluation task statistics on the QA number +and average clues in each category. * The AR task is +designed to evaluate refusal capability. The information +in the question does not exist in the dialogue, and there- +fore clues are unsupported. +Evaluation Tasks Number of QAs Avg. Clues +Extract. & Adapt. +FR 219 2.90 +VS 306 1.91 +TTL 337 4.09 +Reasoning +TR 123 1.74 +VR 174 3.05 +MR 206 2.17 +Knowledge Management +KR 81 2.21 +CD 81 1.70 +AR* 184 — +Overall 1,711 2.69 +A.2.4 Evaluation Data Synthesis +Evaluation QA pairs are constructed through +two complementary and independent generation +pipelines, which jointly contribute to the final eval- +uation set. These two pipelines are designed to +balance coverage, difficulty, and diversity while +ensuring that evaluation instances genuinely cover +long-term and multimodal memory access. +In the first pipeline, advanced LLMs are +prompted to automatically generate candidate QA +pairs. The input to the LLM consists of selected +multi-session conversation histories together with +detailed task descriptions specifying the targeted +evaluation capability (e.g., factual retrieval, tem- +poral reasoning, or knowledge resolution). The +prompts explicitly constrain questions to depend +on information distributed across multiple dialogue +turns or sessions, and, when applicable, to require +visual evidence in addition to textual context. This +pipeline enables scalable generation of QA candi- +dates with broad task coverage. +In parallel, human annotators manually construct +QA pairs by reviewing each entire multi-session +conversation from start to end. Annotators are in- +structed to identify information dependencies that +span long temporal gaps, involve updates or contra- +dictions, or require integrating visual and textual +cues. Based on these observations, annotators de- +sign targeted questions that cannot be answered +from local context alone and instead require ac- +cessing accumulated multimodal memory. Notably, +LLMs are unable to reliably generate QA pairs that +explicitly depend on visual evidence. The result- +ing questions are often inaccurate or of low quality. +Therefore, evaluation instances for tasks that re- +quire explicit visual cues are entirely constructed +by human annotators. This process emphasizes +challenging cases that are difficult for automatic +generation to reliably capture. +For each finalized QA pair, annotators explicitly +annotate the evidence clues required to derive the +correct answer with the assistance of LLMs. These +clues specify the dialogue turns or visual content +where the relevant information is introduced or +referenced. +A.3 Detailed Evaluation Task Descriptions +Mem-Gallery evaluates multimodal long-term con- +versational memory through three complementary +functional dimensions, each consisting of three +fine-grained task types. Together, these nine tasks +provide a systematic and comprehensive assess- +ment of how memory is extracted, adapted, rea- +soned over, and managed under long-horizon mul- +timodal conversational settings: +Memory Extraction & Adaptation. This dimen- +sion focuses on whether an agent can correctly +extract, store, and adaptively utilize information +accumulated throughout long-term multimodal in- +teractions. +• Factual Retrieval (FR): This task evaluates +the model’s ability to accurately recall ex- +plicit factual information mentioned in pre- +vious multimodal dialogue turns, including +user attributes, preferences, entities, events, +and decisions. Questions may require retriev- +ing information introduced long before the +query, testing whether the memory system can +preserve and access long-horizon facts rather +than relying on short-term context. +• Visual-centric Search (VS): This task as- +sesses whether the model can correctly and +explicitly identify or retrieve relevant visual +content from the conversation history. Queries +typically require selecting the correct image +or images associated with a specific entity, +event, or user reference, testing the alignment +between visual memory entries and textual +cues in long-term memory. +• Test-Time Learning (TTL): This task mea- +sures the model’s ability to adapt its under- +standing at inference time based on newly +provided multimodal examples. The agent +must incorporate new multimodal instances + … +User … +Assistant +… +User … +Assistant +… +2024-05-12 +Session-1 +Multi-Round +… +User … +Assistant +… +User … +Assistant +… +2024-08-10 +Session-k +… +User … +Assistant +… +User … +Assistant +… +2024-11-29 +Session-n +… … +Multi-Session +Stored Memory +Memory Agent +Streaming Interaction +… +User … +Assistant +Memory Action: +{Add; Delete; Update} +Figure 8: Structure example of each conversation. +into its existing memory and apply this up- +dated knowledge to answer subsequent ques- +tions, without any parameter updates. This +setting emphasizes online memory adaptation +rather than static memorization. +Memory Reasoning. Beyond direct retrieval, +real-world conversational agents must reason over +stored memories. This dimension evaluates +whether models can perform structured reasoning +using long-term multimodal information accumu- +lated over time. +• Temporal Reasoning (TR): This task evalu- +ates whether the model can reason over tem- +porally ordered information in memory. Ques- +tions may involve comparing events across +different sessions, identifying the order of +occurrences, or determining when a partic- +ular event happened based on dispersed multi- +modal cues. +• Visual-centric Reasoning (VR): This task +tests the model’s ability to use visual infor- +mation as a key component of the reasoning +process. Unlike visual-centric search, which +focuses on retrieval, visual-centric reasoning +requires integrating visual cues with textual +context to infer properties, relationships, or +similarities that are not explicitly stated. +• Multi-entity Reasoning (MR): This task as- +sesses the model’s capacity to jointly reason +over multiple entities stored in memory, where +entities may be introduced at different times +and may involve both textual and visual rep- +resentations. Correct answers require synthe- +sizing information across entities rather than +recalling isolated facts. +Memory Knowledge Management. Conversa- +tional memory is inherently dynamic and imperfect. +This dimension evaluates whether a memory sys- +tem can properly manage evolving, conflicting, or +missing information. +• Knowledge Resolution (KR): This task ex- +amines whether the model can correctly up- +date its memory when new information contra- +dicts previously stored knowledge. The agent +must discard or revise outdated beliefs and +consistently rely on the most recent and cor- +rect information when answering questions. +• Conflict Detection (CD): This task evaluates +the model’s ability to detect inconsistencies +between newly observed information and ex- +isting memory. Rather than resolving the con- +flict, the agent is required to explicitly identify +whether a contradiction exists, testing sensi- +tivity to memory consistency. +• Answer Refusal (AR): This task assesses +whether the model can appropriately refuse to + answer when the requested information has +never been mentioned or is unsupported by +the conversation history. It tests whether the +memory system avoids hallucination and rec- +ognizes the absence of valid evidence in long- +term memory. +A.4 Baseline Details +We include thirteen memory methods, with eight +representative textual memory methods and five +multimodal memory methods. +A.4.1 Textual Memory with Visual Caption +For the textual memory, we transfer the raw image +into an image caption with GPT-5.1. The details of +these models are illustrated as follows. +• Full Memory (Text): It includes all memory +information in textual form as part of the con- +text, and truncates it according to the context +token limit. +• FIFO (First-in-first-out): It selects the most +recent memory information as context accord- +ing to the temporal order of the memory. +• NaiveRAG: It encodes the memory informa- +tion into semantic vectors, then retrieves and +returns the most similar memory entries based +on vector similarity. +• Generative Agent (Park et al., 2023): It intro- +duces a generative agent memory that stores +experiences as language memories and re- +trieves relevant entries to condition agent be- +havior. It further synthesizes higher-level re- +flections from past experiences to support co- +herent long-term planning and interaction. +• Reflexion (Shinn et al., 2023): Reflexion +proposes a verbal memory mechanism in +which agents store self-generated reflections +as episodic memories. After each trial, feed- +back is converted into language reflections +that are appended to memory and reused as +context to guide decisions, enabling trial-and- +error learning via memory accumulation. +• MemGPT (Packer et al., 2023): MemGPT +proposes an OS-inspired memory architec- +ture that treats the LLM context window as +limited working memory and manages long- +term information via hierarchical external stor- +age. Through self-directed function calls, the +model dynamically pages relevant memories +into context and evicts less useful ones, en- +abling effective long-term conversation be- +yond fixed context limits. +• A-Mem (Xu et al., 2025): A-MEM pro- +poses an agentic memory system that orga- +nizes experiences as structured atomic notes +with rich contextual descriptions, inspired +by the Zettelkasten method. New memories +autonomously establish links to related past +memories and can trigger updates to existing +ones, enabling a dynamically evolving mem- +ory network that supports long-term reasoning +without predefined memory operations. +• MemoryOS (Kang et al., 2025): MemoryOS +introduces an operating system–inspired mem- +ory framework that manages agent memory +through hierarchical storage, dynamic updat- +ing, and semantic retrieval. It organizes mem- +ory into short-term, mid-term, and long-term +persona layers, and applies OS-style mecha- +nisms such as FIFO, segmented paging, and +heat-based eviction to maintain coherent and +personalized long-term conversations. +A.4.2 Multimodal Memory +For the multimodal memory, we include five repre- +sentative models. The details of these models are +illustrated as follows. +• Full Memory (Multimodal): It includes all +multimodal memory information as context, +estimates the token consumption of images +using predefined token costs, and truncates +the input according to the context token limit. +• MuRAG (Chen et al., 2022): MuRAG uses a +dense multimodal retriever that encodes both +queries and memory entries into a shared em- +bedding space using a joint vision–language +encoder. At inference time, it performs max- +imum inner product search over an external +memory to retrieve the most relevant items, +which are then used to augment generation. +We use the retrieval paradigm in this paper for +our benchmark implementation. +• UniversalRAG (Yeo et al., 2025): It intro- +duces a modality- and granularity-aware RAG +framework that dynamically routes each query +to the most appropriate knowledge source be- +fore retrieval. Instead of retrieving from a + unified corpus, it first predicts the required +modality and granularity and then performs +targeted retrieval within the selected corpus, +reducing modality bias and retrieval noise. +• NGM (Fisher, 2025): It proposes Neural +Graph Memory, a graph-structured multi- +modal memory that stores episodic expe- +riences as nodes enriched with modality- +specific embeddings and temporal metadata. +Memories are retrieved via graph traversal, en- +abling associative, temporal, and cross-modal +recall beyond flat vector similarity, and sup- +porting long-horizon episodic reasoning. +• AUGUSTUS (Jain et al., 2025): AUGUSTUS +introduces a cognition-inspired multimodal +memory that organizes long-term memory +into recall memory for raw conversation his- +tory and a hierarchical contextual memory for +semantic concepts linked to multimodal con- +text. Abstracting user interactions into seman- +tic tags and retrieving information through a +concept-driven search enables efficient, per- +sonalized, and long-horizon multimodal mem- +ory access beyond flat vector databases. +The corresponding papers and implementation +details of these methods are summarized in Table 7. +For methods without publicly available implemen- +tation code (marked as N/A), we re-implement +them based on the methodological descriptions pro- +vided in the original papers if available. +Table 7: The detailed resource list of models imple- +mented in our benchmark. +Model Venue Paper Implement. Source +Full Memory-Text N/A N/A Link +FIFO N/A N/A Link +NaiveRAG N/A N/A Link +Gen. Agent UIST 2023 Link Link +Reflexion NeurIPS 2023 Link Link +MemGPT Arxiv 2023 Link Link +A-Mem NeurIPS 2025 Link Link +MemoryOS EMNLP 2025 Link Link +Full Memory-Multimodal N/A N/A N/A +MuRAG EMNLP 2022 Link N/A +UniversalRAG Arxiv 2025 Link Link +NGM Arxiv 2025 Link Link +AUGUSTUS Arxiv 2025 Link N/A +A.5 Benchmark Evaluation Details +A.5.1 Experimental Environments +For open-sourced MLLM models, we obtain the of- +ficial models from their corresponding Hugging +Face repositories8. Then, we deploy them on +A100 GPUs under the Linux system with the +vLLM engine9. For closed-sourced MLLM mod- +els, we access them via official APIs. The mem- +ory model implementation and reproduction are +built based on the open-sourced memory frame- +work MemEngine10 (Zhang et al., 2025c). The to- +tal API compute budget for open-sourced MLLM +models was under one thousand US dollars. The +detailed running time for each memory model is +illustrated in Figure 5. +A.5.2 Benchmark Settings +To eliminate the influence of backbone differences +and minimize randomness, all experimental set- +tings use the same MLLM backbone, with a fixed +random seed and the temperature set to 0. To fur- +ther ensure a fair comparison, for memory systems +that involve embedding-based storage and retrieval, +we uniformly adopt the widely used GME-Qwen2- +VL-2B-Instruct (Zhang et al., 2024b) as the embed- +ding model. For memory methods that require a +retriever, we adopt a default retrieval size of K=10. +For other hyperparameters of the models, we retain +their optimal settings from the original codebase. +A.5.3 Evaluation Pipeline +The overall evaluation pipeline is based on the +MemEngine (Zhang et al., 2025c) framework. The +setup is illustrated in Appendix A.5.2. For baseline +methods that involve calling an MLLM module, +we consistently use the corresponding backbone +MLLM of each method to ensure a fair comparison. +Both the conversational data and the evaluation data +are stored in JSON format. For conversational data, +following existing works, dialogue information is +streamed to each memory method at the granular- +ity of dialogue rounds. During question-answering +evaluation, the accumulated memory contents are +concatenated into the context, after which the cor- +responding MLLM is invoked to generate the fi- +nal answer and save the output. For tasks such as +visual-centric search, conflict detection, and answer +refusal, which require specific output formats, ad- +ditional format-constrained prompts are appended +after the question, as shown in Figure 9. Finally, +all generated answers are normalized in a unified +manner before metric computation. The prompt +template used for LLM-as-a-Judge evaluation is +8 +https://huggingface.co +9 +https://github.com/vllm-project/vllm +10 +https://github.com/nuster1128/MemEngine + illustrated in Figure 10. +A.5.4 Evaluation Metrics +We evaluate Mem-Gallery from three key perspec- +tives: question-answering performance, retrieval +effectiveness, and computational efficiency. All +metrics are computed at the instance level and then +averaged over the QA set. +Question-Answering Metrics. For answer qual- +ity, we adopt four widely used metrics in conversa- +tional question answering. +F1 measures token-level overlap between the +predicted answer Ap and the reference answer Ar. +Let Tp and Tr denote the multisets of tokens in Ap +and Ar, respectively. Precision P and recall R are +defined as: +P = +|Tp ∩ Tr| +|Tp| +, R = +|Tp ∩ Tr| +|Tr| +. (4) +The F1 score is then computed as: +F1 = +2PR +P + R +. (5) +Exact Match (EM) evaluates whether the pre- +dicted answer exactly matches the reference answer +after normalization (e.g., lowercasing and punctua- +tion removal): +EM = +( +1, if Ap = Ar, +0, otherwise. +(6) +BLEU-1 measures unigram-level precision be- +tween the predicted answer Ap and the reference +answer Ar. Let countAp (w) denote the number of +occurrences of unigram w in Ap, and countAr (w) +denote its occurrences in Ar. The clipped count +c(w) is defined as: +c(w) = min countAp (w), countAr (w) + +. (7) +BLEU-1 is then computed as: +BLEU-1 = +P +w∈Ap +c(w) +P +w∈Ap +countAp (w) +. (8) +LLM-as-a-Judge evaluates semantic correct- +ness using Qwen-2.5-72B-Instruct as an automatic +evaluator. Given the question, the ground-truth an- +swer, and the model prediction, the judge assigns +a discrete score from the set {0, 0.25, 0.5, 0.75, 1} +according to a predefined rubric. Specifically: +• 0: Incorrect or missing answer, including con- +tradictions or hallucinations. +• 0.25: Poor or tangential answer that touches +on the topic but misses the core entity or value. +• 0.5: Partially correct answer that captures the +main concept but lacks completeness or key +details. +• 0.75: Largely correct answer with only minor +omissions or imprecision. +• 1: Correct and exact answer that fully matches +the ground truth. +The final LLM-as-a-Judge score is obtained by av- +eraging the assigned scores across evaluation in- +stances. This metric complements lexical overlap +metrics by capturing semantic equivalence, para- +phrasing, and partial correctness. +For the Conflict Detection (CD) task, the model +is required to output a single binary token (Yes or +No). Since this is a binary classification task and +all evaluated models reliably follow the instruction +format, the values of F1, EM, BLEU-1, and LLM- +as-a-Judge coincide for this task. +Retrieval Effectiveness Metrics. To assess mem- +ory access quality independently of answer gen- +eration, we adopt standard information retrieval +metrics. For each evaluation query, let RK denote +the set of top-K retrieved memory entries, and G +denote the set of ground-truth relevant memory +entries annotated as evidence clues. +Recall@K is defined as: +Recall@K = +|RK ∩ G| +|G| +. (9) +Precision@K is defined as: +Precision@K = +|RK ∩ G| +|RK| +. (10) +Hit@K measures whether at least one relevant +memory entry is retrieved: +Hit@K = +( +1, if RK ∩ G ̸= ∅, +0, otherwise. +(11) +All retrieval metrics are first computed per query +and then averaged across the evaluation set. + Format Restriction Prompts +Visual-centric Search: +Return the image_id of the image(s). If there are multiple images, sort them in ascending order and +separate them by commas. Format example: “D2:IMG_003, D2:IMG_010, D10:IMG_002” (for +format reference only). +Conflict Detection: +Please check whether this information conflicts with the conversation, and reply strictly with either +“Yes.” or “No.” +Answer Refusal: +Provide your answer based on the information in the conversation. Only if the information about +the question is not present in the conversation, reply with: “Not mentioned.” +Figure 9: Prompts for format restriction tasks. +Efficiency Metrics. We evaluate efficiency from +a system-level perspective. For each model, we +measure: (1) Information Memorization Time, +defined as the cumulative wall-clock time spent +processing and storing conversational inputs during +interaction. (2) Memory Retrieval and Answer +Generation Time, defined as the wall-clock time +required to retrieve memory entries and generate +the final response at evaluation time. All efficiency +metrics are reported in seconds. +A.6 Additional Results and Analysis +A.6.1 MLLM Backbone Analysis +To analyze how the choice of MLLM backbone +influences memory performance, we evaluate rep- +resentative open-source and closed-source MLLMs +under a unified memory framework. Specifically, +Table 8 and Table 3 report results on the open- +source Qwen-2.5-VL-3B and Qwen-2.5-VL-7B +backbones, while Table 9 and Table 10 report +results on the closed-source GPT-4.1-Nano and +Gemini-2.5-Flash-Lite backbones. Moreover, Fig- +ure 11, Figure 12, Figure 13, and Figure 14 illus- +trate the radar chart of three representative textual +memory models (MemGPT, A-Mem, and Memo- +ryOS) and three representative multimodal memory +models (MuRAG, UniversalRAG, and AUGUS- +TUS) to clearly present the advantages and disad- +vantages of different subtasks. Across all settings, +the same set of textual and multimodal memory +methods is applied, enabling a controlled compari- +son of backbone effects. +Overall effect of backbone capability. Across +both open-source and closed-source backbones, +stronger MLLMs consistently improve absolute +memory performance across most tasks and meth- +ods. This trend is visible when scaling from +Qwen-2.5-VL-3B to Qwen-2.5-VL-7B, and further +from GPT-4.1-Nano to Gemini-2.5-Flash-Lite. The +gains are systematic rather than isolated, indicat- +ing that increased backbone capacity enhances the +model’s ability to consume retrieved memory and +generate coherent responses. However, these im- +provements do not fundamentally alter which task +categories remain difficult. Reasoning-intensive +tasks and knowledge management tasks continue +to exhibit lower performance relative to extraction- +oriented tasks, even under the strongest backbones. +This suggests that backbone scaling primarily im- +proves memory utilization, rather than resolving +intrinsic limitations in long-horizon reasoning and +memory consistency of existing memory methods. +Backbone scaling amplifies memory design +quality rather than compensating for it. Despite +large differences in backbone capacity and model +family, the relative ranking of memory methods +remains largely stable across all evaluated settings. +Well-organized memory systems consistently out- +perform naïve or unstructured baselines under both +weak and strong backbones, regardless of whether +the memory is textual or multimodal. This stabil- +ity indicates that backbone scaling does not com- +pensate for poor memory organization. Instead, +stronger backbones tend to amplify existing dif- +ferences between memory designs. Multimodal +memory benefits from stronger backbones only +when cross-modal alignment and retrieval quality +are sufficiently well controlled. Otherwise, strong +textual memory can achieve comparable effective- +ness with lower complexity, such as the equipment + LLM-as-a-Judge Prompt +You are an impartial judge evaluating the memory capabilities of an AI assistant with the question- +answering task. Your task is to compare the Assistant’s Answer against the Ground Truth and +assign a score of 0, 0.25, 0.5, 0.75, or 1. +Scoring Rubric +Score 0 (Incorrect / Miss): +- The answer contradicts the Ground Truth. +- For Yes/No questions: The answer has the wrong polarity (e.g., says "Yes" when Ground Truth is +"No"). +- For Open-ended questions: The answer provides factually wrong information or hallucinations. +- The assistant fails to provide the required information. +Score 0.25 (Poor / Tangential): +- The answer touches on the topic but misses the core entity or key value required. +- The answer contains a mix of minor correct details and significant hallucinations or wrong +associations. +- The answer is excessively vague to the point of being useless (e.g., answering "a dog" instead of +"a golden retriever"). +Score 0.5 (Partial / Vague): +- The answer is technically correct, but lacks confidence or is incomplete. +- The answer captures the main entity or concept correctly but misses a part of the required +supporting details. +- For Yes/No questions: The polarity is correct, but the reasoning is flawed (if have), or the assistant +is uncertain (e.g., "I think it might be Yes"). +- For Open-ended questions: The answer is too general or misses key adjectives/details present in +the Ground Truth. +Score 0.75 (Good / Minor Imperfection): +- The answer is largely accurate and captures the core information confidently. +- It misses only minor details (e.g., specific adjectives or secondary details) that do not alter the +main truth. +- The answer contains all the correct information but includes unnecessary "fluff" or slight conver- +sational filler that reduces precision. +Score 1 (Correct / Exact): +- The answer is accurate, precise, and confident. +- For Yes/No questions: The polarity matches the Ground Truth perfectly. +- For Open-ended questions: The answer contains all the core information and necessary details +required by the Ground Truth without hallucinations. +Input Data +Question: {question} +Ground Truth: {ground_truth} +Assistant Answer: {model_output} +Output Format +Output strictly in the following JSON format: "score": <0, 0.25, 0.5, 0.75, or 1>, "reasoning": +"" +Figure 10: The prompt of the LLM-as-a-Judge metric for Qwen-2.5-72B-Instruct. +with Gemini-2.5-Flash-Lite. +Reasoning remains a bottleneck despite +stronger backbones. Although stronger back- +bones lead to consistent gains on reasoning- + FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +F1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +BLEU-1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +EM +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +LLM-as-a-Judge +MemGPT +A-Mem +MemoryOS +MuRAG +UniversalRAG +AUGUSTUS +Figure 11: Radar chart of subtask performance for rep- +resentative memory models on Qwen-2.5-VL-3B. +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +F1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +BLEU-1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +EM +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +LLM-as-a-Judge +MemGPT +A-Mem +MemoryOS +MuRAG +UniversalRAG +AUGUSTUS +Figure 12: Radar chart of subtask performance for rep- +resentative memory models on Qwen-2.5-VL-7B. +oriented tasks, including temporal reasoning, +visual-centric reasoning, and multi-entity reason- +ing, the magnitude of these gains is noticeably +smaller than those observed in extraction-focused +tasks. This pattern is consistent across both open- +source and closed-source backbones. In several +reasoning subtasks, the performance gap between +textual and multimodal memory narrows as back- +bone capability increases, and well-designed tex- +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +F1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +BLEU-1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +EM +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +LLM-as-a-Judge +MemGPT +A-Mem +MemoryOS +MuRAG +UniversalRAG +AUGUSTUS +Figure 13: Radar chart of subtask performance for rep- +resentative memory models on GPT-4.1-Nano. +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +F1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +BLEU-1 +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +EM +FR +VS +TTL +TR +VR +MR +KR +CD +AR +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 +LLM-as-a-Judge +MemGPT +A-Mem +MemoryOS +MuRAG +UniversalRAG +AUGUSTUS +Figure 14: Radar chart of subtask performance for rep- +resentative memory models on Gemini-2.5-Flash-Lite. +tual memory methods remain competitive with mul- +timodal approaches. These observations indicate +that long-horizon reasoning over multimodal mem- +ory is not primarily constrained by backbone capac- +ity, but rather by how multimodal information is +structured, linked, and jointly reasoned over within +the memory system. These results indicate that the +organization and maintenance of multimodal mem- +ory mechanisms remain an open and promising + direction for future research. +Limited sensitivity in knowledge management +tasks. For knowledge management tasks, includ- +ing knowledge resolution, conflict detection, and +answer refusal, improvements from stronger back- +bones are relatively limited across all evaluated +MLLMs. Both open-source and closed-source +backbones exhibit similar qualitative behavior, with +only marginal performance differences attributable +to backbone choice. This suggests diminishing +returns from backbone scaling for safety- and +consistency-oriented memory behaviors, which +rely more heavily on explicit memory state track- +ing, conflict awareness, and decision policies than +on raw generative or perceptual capacity. +A.6.2 Memory Retrieval Analysis +This section analyzes how the number of re- +trieved memory entries affects both retrieval effec- +tiveness and downstream task performance. Ta- +ble 11, Table 3, Table 12, and Table 13 report +task-level QA results under different retrieval sizes +K ∈ {5, 10, 15, 20}. Figure 15 shows the over- +all task performance trend under K value changes. +Based on the fine-grained annotated clues for each +evaluation data, Table 14 summarizes the corre- +sponding retrieval metrics, including Recall@K, +Precision@K, and Hit@K for retrieval behavior +analysis. All results are evaluated on the Qwen-2.5- +VL-7B backbone. +Impact of Retrieval Size on Downstream Task +Performance. The results in Table 11, Table 3, +Table 12, and Table 13 provide a continuous view +of how retrieval quantity affects memory utiliza- +tion and task performance. Across most memory +models and task categories, we generally observe a +non-monotonic relationship between retrieval size +and downstream performance. Increasing the re- +trieval size from a small value (K=5) to a moderate +range (K=10 or K=15) generally leads to consis- +tent improvements, especially for memory extrac- +tion and adaptation tasks such as factual retrieval, +visual-centric search, and test-time learning. +However, further increasing the retrieval size to +K=20 does not consistently yield additional gains. +As shown in Table 13, performance often satu- +rates or fluctuates across tasks, and in some cases +slightly degrades compared to the K=10 or K=15 +settings. This pattern indicates that retrieving more +memory entries beyond a moderate threshold does +not necessarily translate into better task outcomes. +This effect is particularly pronounced for multi- +5 10 15 20 +K +0.35 +0.40 +0.45 +0.50 +0.55 +0.60 +0.65 +0.70 +F1 +5 10 15 20 +K +0.25 +0.30 +0.35 +0.40 +0.45 +0.50 +EM +Gen. Agent +NaiveRAG +A-Mem +MemoryOS +MuRAG +UniversalRAG +NGM +AUGUSTUS +Figure 15: Parameter study on the retrieval size K. +modal memory methods. While larger retrieval +sizes improve coverage of relevant multimodal en- +tries, they also introduce additional redundant or +weakly aligned visual–textual information. As a re- +sult, the benefits of higher recall are partially offset +by increased noise in the retrieved memory, limit- +ing the effectiveness of downstream reasoning and +answer generation. In contrast, moderate retrieval +sizes such as K=10, as reflected in Table 3, appear +to offer a better balance between relevance and +redundancy for multimodal memory utilization. +For reasoning-oriented tasks, including tempo- +ral reasoning, visual-centric reasoning, and multi- +entity reasoning, the impact of retrieval size is even +more constrained. Performance differences across +K=5, K=10, K=15, and K=20 are relatively small, +suggesting that reasoning performance is less sen- +sitive to retrieval quantity and more dependent on +how retrieved memory is structured and integrated. +This observation holds consistently across both tex- +tual and multimodal memory methods. +Knowledge management tasks, such as knowl- +edge resolution, conflict detection, and answer re- +fusal, exhibit the weakest sensitivity to retrieval +size. Across all values of K, task performance +remains relatively stable, indicating that simply +retrieving more memory entries is insufficient to +improve consistency-oriented behaviors and knowl- +edge management capability. +Overall, these results suggest that moderate re- +trieval sizes, exemplified by K=10, often provide +the most effective trade-off between retrieval cov- +erage and memory usability. Increasing retrieval +size beyond this range yields diminishing returns, +particularly for multimodal memory systems, high- +lighting the importance of selective and relevance- +aware retrieval rather than indiscriminate scaling +of retrieved memory entries. +Retrieval Effectiveness versus Memory Uti- +lization. Table 14 provides further insight into why +increasing retrieval size does not consistently im- +prove downstream task performance observed in + Table 8: Evaluation results on Qwen-2.5-VL-3B. The best and second-performed memory model(s) are highlighted +with orange and blue backgrounds. +Qwen-2.5-VL-3B Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2501 0.1578 0.5664 0.2301 0.2467 0.4171 0.5985 0.5889 0.2282 0.6504 0.6601 0.6045 0.5660 +BLEU-1 0.2000 0.1138 0.4862 0.1847 0.1976 0.3494 0.4937 0.4897 0.1795 0.5673 0.5811 0.5290 0.4868 +EM 0.0959 0.0502 0.2740 0.0868 0.0959 0.1872 0.2843 0.3014 0.0868 0.3699 0.3699 0.3470 0.3105 +LLM-Judge 0.3105 0.1918 0.7443 0.2922 0.3082 0.5845 0.7690 0.7694 0.2717 0.8584 0.8516 0.7694 0.7626 +VS +F1 0.2687 0.1175 0.7424 0.3244 0.2691 0.2631 0.7653 0.7771 0.2154 0.8661 0.8395 0.8493 0.8537 +BLEU-1 0.2466 0.1012 0.6934 0.2826 0.2471 0.2381 0.7096 0.7123 0.1959 0.8337 0.8056 0.8066 0.8047 +EM 0.1569 0.0359 0.5033 0.1634 0.1601 0.1176 0.5802 0.5458 0.0980 0.6307 0.6275 0.6569 0.6242 +LLM-Judge 0.2337 0.0801 0.7092 0.2696 0.2337 0.2843 0.6794 0.7565 0.1797 0.8676 0.8350 0.8284 0.8464 +TTL +F1 0.4657 0.3640 0.6769 0.5454 0.4633 0.4833 0.6448 0.6595 0.4857 0.7966 0.7227 0.7732 0.7328 +BLEU-1 0.4279 0.3161 0.6253 0.4845 0.4251 0.4427 0.5884 0.5997 0.4397 0.7402 0.6655 0.7145 0.6803 +EM 0.3294 0.2344 0.5104 0.3769 0.3234 0.3620 0.4927 0.4777 0.3353 0.6261 0.5668 0.6083 0.5786 +LLM-Judge 0.6365 0.5415 0.7433 0.6706 0.6380 0.6276 0.7080 0.7567 0.6320 0.8338 0.7982 0.8294 0.8027 +Reasoning +TR +F1 0.2866 0.2398 0.4785 0.3288 0.2866 0.3996 0.6036 0.4942 0.2972 0.5700 0.5484 0.5332 0.5422 +BLEU-1 0.2688 0.2226 0.4454 0.3036 0.2683 0.3728 0.5817 0.4654 0.2807 0.5369 0.5119 0.4986 0.5072 +EM 0.1301 0.0976 0.3089 0.1626 0.1301 0.2114 0.4569 0.3089 0.1382 0.3984 0.3821 0.3659 0.3740 +LLM-Judge 0.3252 0.2480 0.6667 0.3821 0.3211 0.5813 0.6940 0.6707 0.3130 0.7602 0.7886 0.6951 0.7561 +VR +F1 0.2545 0.1532 0.2966 0.1953 0.2552 0.3226 0.3441 0.3860 0.2285 0.4440 0.4554 0.4260 0.4681 +BLEU-1 0.2398 0.1384 0.2778 0.1823 0.2395 0.3065 0.3319 0.3685 0.2176 0.4230 0.4352 0.4100 0.4504 +EM 0.2011 0.1092 0.1897 0.1379 0.2011 0.2414 0.2806 0.2931 0.1839 0.3333 0.3333 0.3333 0.3563 +LLM-Judge 0.3161 0.1810 0.3822 0.2615 0.3190 0.4310 0.4101 0.5172 0.2615 0.5374 0.5489 0.5201 0.5690 +MR +F1 0.2600 0.1934 0.4380 0.2683 0.2615 0.3400 0.4401 0.4483 0.2400 0.4851 0.4908 0.4697 0.4773 +BLEU-1 0.1943 0.1364 0.3277 0.1997 0.1969 0.2599 0.3162 0.3474 0.1787 0.3736 0.3788 0.3602 0.3639 +EM 0.0291 0.0243 0.0825 0.0388 0.0291 0.0340 0.0722 0.0971 0.0194 0.0874 0.0922 0.0874 0.1019 +LLM-Judge 0.4442 0.2985 0.7670 0.4345 0.4442 0.6092 0.7474 0.7597 0.4223 0.8083 0.8301 0.7985 0.7840 +Knowledge +Management +KR +F1 0.2034 0.1563 0.3382 0.2613 0.2061 0.2372 0.4280 0.3706 0.2007 0.3975 0.3883 0.3441 0.3604 +BLEU-1 0.1627 0.1118 0.2754 0.2085 0.1639 0.1869 0.3693 0.3094 0.1625 0.3358 0.3323 0.2842 0.2987 +EM 0.0741 0.0370 0.1111 0.0864 0.0741 0.0741 0.2267 0.1235 0.0864 0.1481 0.1728 0.1358 0.1235 +LLM-Judge 0.4012 0.2963 0.5926 0.4012 0.3951 0.5185 0.6600 0.6852 0.3704 0.6790 0.6852 0.6420 0.6914 +CD +F1 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 +BLEU-1 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 +EM 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 +LLM-Judge 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 +AR +F1 0.9742 0.9783 0.8942 0.9578 0.9742 0.8387 0.9080 0.8992 0.9647 0.8823 0.8661 0.8707 0.8553 +BLEU-1 0.9730 0.9783 0.8924 0.9571 0.9730 0.8355 0.9080 0.8982 0.9628 0.8811 0.8648 0.8702 0.8540 +EM 0.9728 0.9783 0.8913 0.9565 0.9728 0.8315 0.9080 0.8967 0.9620 0.8804 0.8641 0.8696 0.8533 +LLM-Judge 0.9620 0.9620 0.8804 0.9429 0.9620 0.8234 0.9052 0.8804 0.9484 0.8641 0.8478 0.8587 0.8424 +Overall +F1 0.3798 0.2986 0.5832 0.4013 0.3793 0.4165 0.6059 0.6013 0.3665 0.6679 0.6479 0.6443 0.6383 +BLEU-1 0.3492 0.2689 0.5333 0.3620 0.3487 0.3794 0.5505 0.5462 0.3360 0.6194 0.5995 0.5953 0.5881 +EM 0.2624 0.2040 0.3933 0.2671 0.2618 0.2700 0.4303 0.4103 0.2496 0.4728 0.4594 0.4670 0.4541 +LLM-Judge 0.4541 0.3521 0.6856 0.4597 0.4538 0.5383 0.6886 0.7162 0.4272 0.7756 0.7662 0.7463 0.7528 +Table 3 and Table 11-13. As the retrieval size K +increases, retrieval effectiveness metrics exhibit a +clear and expected trend: Recall@K and Hit@K +consistently improve across all retrievers, indicat- +ing that more relevant memory entries are success- +fully retrieved. This trend holds for both textual +and multimodal retrievers, and is especially pro- +nounced for multimodal methods such as MuRAG +and UniversalRAG, which achieve high recall even +at moderate K value. +However, this improvement in recall is accom- +panied by a systematic decline in Precision@K, +particularly as K increases beyond moderate val- +ues. While larger K values ensure broader cover- +age of relevant memory entries, they also introduce +a growing number of weakly relevant or redundant +items. As a result, the retrieved memory becomes +increasingly noisy, especially in multimodal set- +tings where cross-modal alignment is imperfect. +This divergence explains the observed decoupling +between retrieval effectiveness and downstream +task performance. Although Recall@K continues +to improve from K=10 to K=20, task performance +in Table 13 often saturates or fluctuates relative to +Table 3 and Table 12. In other words, higher re- +call does not guarantee better memory utilization. +The additional retrieved entries do not consistently +contribute useful evidence for reasoning or answer +generation, and may instead distract the model dur- +ing inference. +The effect is more evident for multimodal +memory systems. Compared to textual memory, +multimodal retrieval introduces heterogeneous vi- +sual–textual information, making it more sensitive +to redundancy and misalignment. Consequently, +multimodal methods benefit most from moderate +retrieval sizes that balance recall and precision, as +exemplified by the strong performance at K=10. +Beyond this range, further recall gains are offset by +increased retrieval noise, limiting improvements in +downstream tasks. +Overall, these results indicate that retrieval qual- +ity, rather than retrieval quantity, is the domi- +nant factor governing downstream memory per- +formance. Simply scaling the retrieval size im- +proves recall metrics but does not necessarily im- + Table 9: Evaluation results on GPT-4.1-Nano. The best and second-performed memory model(s) are highlighted +with orange and blue backgrounds. +GPT-4.1-Nano Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2279 0.1379 0.5498 0.1672 0.2277 0.5983 0.6048 0.5848 0.1920 0.6256 0.6060 0.5922 0.5666 +BLEU-1 0.1724 0.0907 0.4682 0.1164 0.1719 0.5193 0.5194 0.4936 0.1413 0.5392 0.5189 0.5036 0.4734 +EM 0.0685 0.0274 0.2374 0.0228 0.0594 0.2740 0.2648 0.2466 0.0548 0.2785 0.2511 0.2785 0.2466 +LLM-Judge 0.2260 0.0936 0.7740 0.1918 0.2283 0.8082 0.8151 0.8425 0.1872 0.8653 0.8356 0.7968 0.7922 +VS +F1 0.2330 0.0839 0.7457 0.1787 0.2356 0.6853 0.7965 0.7639 0.1318 0.8478 0.8410 0.8329 0.8354 +BLEU-1 0.2175 0.0689 0.7021 0.1507 0.2210 0.6573 0.7553 0.6962 0.1155 0.8092 0.8109 0.7906 0.7831 +EM 0.1732 0.0294 0.5294 0.0980 0.1732 0.5392 0.5882 0.5621 0.0588 0.6275 0.6340 0.6340 0.6373 +LLM-Judge 0.2124 0.0507 0.6944 0.1585 0.2124 0.6536 0.7761 0.7255 0.0997 0.8431 0.8252 0.8056 0.8121 +TTL +F1 0.2278 0.1473 0.3618 0.2021 0.2226 0.2866 0.3279 0.3263 0.1991 0.4395 0.4407 0.5273 0.4488 +BLEU-1 0.1609 0.0974 0.2523 0.1414 0.1567 0.1969 0.2232 0.2346 0.1406 0.3468 0.3581 0.4372 0.3491 +EM 0.0504 0.0148 0.0504 0.0297 0.0445 0.0386 0.0326 0.0742 0.0356 0.1662 0.1958 0.2641 0.1424 +LLM-Judge 0.5742 0.4585 0.7834 0.5801 0.5579 0.6588 0.8027 0.7389 0.5015 0.7804 0.8012 0.8709 0.8145 +Reasoning +TR +F1 0.2035 0.1356 0.4381 0.2028 0.1997 0.4306 0.5116 0.5017 0.1916 0.5007 0.4366 0.4924 0.4808 +BLEU-1 0.1744 0.1143 0.4032 0.1746 0.1694 0.3932 0.4730 0.4649 0.1682 0.4676 0.3997 0.4606 0.4484 +EM 0.0569 0.0488 0.2764 0.0894 0.0650 0.2520 0.3415 0.3171 0.0650 0.3252 0.2683 0.3496 0.3252 +LLM-Judge 0.2764 0.1382 0.7154 0.2886 0.2846 0.7276 0.7846 0.8130 0.2398 0.7520 0.7480 0.7236 0.7154 +VR +F1 0.2011 0.0714 0.2041 0.0987 0.2000 0.4140 0.3269 0.3697 0.0909 0.3426 0.4417 0.3043 0.3817 +BLEU-1 0.1796 0.0491 0.1791 0.0747 0.1772 0.3855 0.2979 0.3431 0.0690 0.3077 0.4146 0.2738 0.3518 +EM 0.1379 0.0172 0.0862 0.0172 0.1322 0.2931 0.1954 0.2586 0.0345 0.2069 0.3218 0.1724 0.2529 +LLM-Judge 0.3276 0.1379 0.3736 0.1810 0.3305 0.6207 0.5661 0.5862 0.2011 0.5575 0.6638 0.4713 0.5747 +MR +F1 0.2415 0.1814 0.4490 0.2354 0.2389 0.4428 0.4669 0.4523 0.2017 0.5024 0.4670 0.4570 0.4829 +BLEU-1 0.1720 0.1174 0.3402 0.1704 0.1710 0.3388 0.3602 0.3479 0.1385 0.3946 0.3670 0.3506 0.3798 +EM 0.0194 0.0097 0.0388 0.0097 0.0146 0.0388 0.0437 0.0583 0.0097 0.0534 0.0534 0.0534 0.0680 +LLM-Judge 0.2451 0.0922 0.7985 0.2597 0.2500 0.7985 0.8617 0.8325 0.1942 0.8617 0.7816 0.8180 0.8180 +Knowledge +Management +KR +F1 0.1454 0.1001 0.2852 0.1408 0.1411 0.3733 0.3283 0.2873 0.1293 0.3626 0.2972 0.2878 0.2607 +BLEU-1 0.0965 0.0633 0.2174 0.0960 0.0934 0.3017 0.2556 0.2190 0.0902 0.2897 0.2316 0.2252 0.1958 +EM 0.0123 0.0000 0.0494 0.0000 0.0123 0.1111 0.0741 0.0370 0.0123 0.0988 0.0494 0.0864 0.0617 +LLM-Judge 0.2840 0.1975 0.6049 0.2407 0.2963 0.7593 0.6852 0.6790 0.2531 0.7469 0.6543 0.6173 0.5741 +CD +F1 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 +BLEU-1 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 +EM 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 +LLM-Judge 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 +AR +F1 0.9682 0.9891 0.8406 0.9751 0.9737 0.7911 0.8496 0.8874 0.9791 0.9039 0.9102 0.9382 0.9058 +BLEU-1 0.9679 0.9891 0.8323 0.9738 0.9733 0.7811 0.8430 0.8831 0.9788 0.9003 0.9058 0.9335 0.9009 +EM 0.9674 0.9891 0.8261 0.9728 0.9728 0.7717 0.8370 0.8804 0.9783 0.8967 0.9022 0.9293 0.8967 +LLM-Judge 0.9565 0.9728 0.8587 0.9647 0.9592 0.8370 0.8668 0.8940 0.9620 0.9022 0.9103 0.9321 0.9022 +Overall +F1 0.3084 0.2291 0.5057 0.2769 0.3075 0.5034 0.5380 0.5339 0.2636 0.5832 0.5774 0.5849 0.5703 +BLEU-1 0.2703 0.1974 0.4436 0.2389 0.2698 0.4480 0.4764 0.4705 0.2293 0.5243 0.5236 0.5266 0.5080 +EM 0.1923 0.1420 0.2800 0.1596 0.1899 0.2987 0.3092 0.3203 0.1572 0.3518 0.3600 0.3746 0.3489 +LLM-Judge 0.3966 0.2779 0.7046 0.3720 0.3960 0.7063 0.7651 0.7507 0.3346 0.7814 0.7747 0.7659 0.7583 +prove task outcomes. Effective multimodal mem- +ory systems therefore require selective, relevance- +aware retrieval strategies that prioritize precision +and alignment over exhaustive retrieval. Therefore, +this highlights the need for future work to consider +the principle that “less but high-quality is more” in +multimodal memory design (Chen et al., 2025). +A.7 Case Study +We further present a qualitative case study to illus- +trate how representative memory systems behave +under different multimodal long-term memory chal- +lenges in Mem-Gallery. We select three textual +memory models (NaiveRAG, A-Mem, MemoryOS) +and three multimodal memory models (MuRAG, +UniversalRAG, AUGUSTUS) and analyze their re- +sponses on three representative subtasks. These +cases correspond to test-time learning of the mem- +ory extraction & adaptation task, visual-centric rea- +soning of the memory reasoning task, and knowl- +edge resolution of the memory knowledge manage- +ment task, as shown in Figure 16, Figure 17, and +Figure 18, respectively. From the case studies, we +can have the following observations. +Figure 16 presents a test-time learning scenario +in which the agent must adapt to newly introduced +visual exemplars during the conversation and gen- +eralize the learned concept at inference time. The +example requires identifying the clothing category +based on a sequence of visual demonstrations and +prior conversational cues. Although textual mem- +ory baselines are augmented with high-quality vi- +sual captions, textual memory methods still exhibit +clear limitations. Although NaiveRAG and A-Mem +retrieve relevant textual clues, they lack the ability +to integrate newly observed visual patterns, result- +ing in generic or incorrect predictions. MemoryOS +exhibits a similar limitation, indicating that strong +textual organization alone is insufficient when the +task requires visual abstraction at inference time. +In contrast, multimodal memory methods that ex- +plicitly preserve visual representations demonstrate +stronger adaptability. MuRAG and AUGUSTUS +successfully align the new image with previously +observed visual examples and correctly infer the +category. This case highlights the limitation of +caption-based textual memory. While high-quality + Table 10: Evaluation results on Gemini-2.5-Flash-Lite. The best and second-performed memory model(s) are +highlighted with orange and blue backgrounds. +Gemini-2.5-Flash-Lite Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2014 0.1014 0.5905 0.2329 0.2014 0.6667 0.5266 0.6622 0.1534 0.6888 0.7014 0.6341 0.6011 +BLEU-1 0.1652 0.0732 0.4999 0.1802 0.1652 0.5633 0.4270 0.5683 0.1214 0.6009 0.6136 0.5510 0.5141 +EM 0.0822 0.0320 0.2648 0.0868 0.0822 0.2557 0.1690 0.3425 0.0594 0.3653 0.3562 0.3333 0.3151 +LLM-Judge 0.2237 0.0868 0.7922 0.2945 0.2215 0.9475 0.8402 0.8562 0.1507 0.8927 0.9064 0.8082 0.7922 +VS +F1 0.2252 0.0598 0.7909 0.3450 0.2252 0.9204 0.8451 0.8427 0.1051 0.9077 0.9175 0.8753 0.8811 +BLEU-1 0.2102 0.0553 0.7539 0.3156 0.2102 0.8957 0.8019 0.7918 0.0929 0.8837 0.8919 0.8456 0.8443 +EM 0.1863 0.0327 0.6078 0.2451 0.1863 0.8105 0.6797 0.6536 0.0654 0.7909 0.7745 0.7484 0.7157 +LLM-Judge 0.2141 0.0458 0.7663 0.3170 0.2141 0.9265 0.8415 0.8350 0.0931 0.9052 0.9199 0.8611 0.8775 +TTL +F1 0.4697 0.2002 0.7839 0.6149 0.4697 0.8517 0.7173 0.8360 0.3031 0.8750 0.8464 0.8477 0.8549 +BLEU-1 0.4322 0.1260 0.7259 0.5552 0.4322 0.8012 0.6229 0.7709 0.2654 0.8130 0.7689 0.7745 0.7908 +EM 0.3501 0.0712 0.6113 0.4481 0.3501 0.6795 0.4243 0.6380 0.1958 0.6677 0.6410 0.6350 0.6499 +LLM-Judge 0.5341 0.2507 0.8680 0.6869 0.5341 0.9021 0.9006 0.9021 0.3442 0.9258 0.8976 0.9184 0.9139 +Reasoning +TR +F1 0.2105 0.1447 0.4801 0.2201 0.2105 0.5475 0.4714 0.5325 0.1796 0.5806 0.5675 0.5280 0.5555 +BLEU-1 0.1953 0.1345 0.4513 0.2046 0.1953 0.5071 0.4301 0.5047 0.1637 0.5507 0.5392 0.5011 0.5267 +EM 0.0976 0.0407 0.3333 0.0732 0.0976 0.3496 0.2927 0.3821 0.0650 0.4309 0.4146 0.3984 0.4065 +LLM-Judge 0.2317 0.1423 0.6870 0.2846 0.2317 0.8902 0.7967 0.7358 0.1911 0.7886 0.8089 0.6992 0.7886 +VR +F1 0.1909 0.0423 0.3135 0.1466 0.1909 0.6294 0.4262 0.3997 0.0798 0.4470 0.4954 0.4422 0.4533 +BLEU-1 0.1819 0.0356 0.2985 0.1398 0.1819 0.6083 0.4075 0.3843 0.0707 0.4267 0.4785 0.4200 0.4325 +EM 0.1494 0.0230 0.2241 0.1092 0.1494 0.5230 0.3276 0.3103 0.0517 0.3563 0.4023 0.3563 0.3563 +LLM-Judge 0.2443 0.0402 0.4138 0.2443 0.2443 0.7845 0.5603 0.5805 0.1063 0.6264 0.6782 0.5920 0.5718 +MR +F1 0.1648 0.0834 0.4521 0.1765 0.1648 0.5210 0.4529 0.5161 0.1268 0.5137 0.5137 0.4783 0.4691 +BLEU-1 0.1250 0.0594 0.3508 0.1265 0.1250 0.4130 0.3553 0.4111 0.0987 0.4032 0.4011 0.3703 0.3575 +EM 0.0097 0.0097 0.0631 0.0097 0.0097 0.0680 0.0388 0.0825 0.0097 0.0680 0.0728 0.0437 0.0631 +LLM-Judge 0.2184 0.0922 0.7767 0.2743 0.2184 0.9272 0.8519 0.8568 0.1481 0.8714 0.8738 0.8058 0.7864 +Knowledge +Management +KR +F1 0.1157 0.0604 0.2813 0.1050 0.1157 0.3915 0.3341 0.3609 0.0918 0.4037 0.3710 0.3111 0.3168 +BLEU-1 0.0848 0.0370 0.2177 0.0810 0.0848 0.3225 0.2644 0.2956 0.0673 0.3534 0.3156 0.2607 0.2631 +EM 0.0247 0.0000 0.0864 0.0247 0.0247 0.1605 0.0988 0.1605 0.0247 0.1975 0.1481 0.1358 0.1235 +LLM-Judge 0.2531 0.1296 0.6235 0.2037 0.2531 0.8025 0.7593 0.7840 0.1728 0.7716 0.7222 0.5988 0.6667 +CD +F1 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.4938 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 +BLEU-1 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.4938 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 +EM 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.4938 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 +LLM-Judge 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.5123 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 +AR +F1 0.9644 0.9946 0.9122 0.9688 0.9644 0.8512 0.9175 0.9220 0.9844 0.9242 0.9186 0.9332 0.9339 +BLEU-1 0.9634 0.9946 0.9102 0.9682 0.9634 0.8454 0.9155 0.9205 0.9841 0.9218 0.9162 0.9318 0.9321 +EM 0.9620 0.9946 0.9076 0.9674 0.9620 0.8370 0.9130 0.9185 0.9837 0.9185 0.9130 0.9293 0.9293 +LLM-Judge 0.9511 0.9783 0.8995 0.9592 0.9511 0.8478 0.9212 0.9212 0.9674 0.9130 0.9076 0.9212 0.9239 +Overall +F1 0.3408 0.2158 0.6282 0.3936 0.3408 0.7202 0.6294 0.6861 0.2627 0.7155 0.7157 0.6901 0.6877 +BLEU-1 0.3178 0.1913 0.5796 0.3608 0.3178 0.6706 0.5702 0.6327 0.2424 0.6676 0.6644 0.6400 0.6370 +EM 0.2595 0.1555 0.4436 0.2858 0.2595 0.5219 0.4120 0.4904 0.1940 0.5283 0.5207 0.5079 0.5020 +LLM-Judge 0.3729 0.2236 0.7452 0.4407 0.3726 0.8755 0.8115 0.8165 0.2764 0.8437 0.8472 0.8030 0.8057 +captions can convey semantic details, they remain +a lossy and static representation of visual infor- +mation, making it difficult for models to perform +visual pattern induction and concept generalization +at test time. Effective test-time learning therefore +requires memory systems that can retain and com- +pare visual evidence directly, rather than relying +solely on textualized visual descriptions. +Figure 17 illustrates a visual-centric reasoning +task, where the model must determine whether the +dog in a given image matches the breed of a pet +mentioned earlier in the conversation. This case +reveals a more subtle failure mode. Several models +retrieve the correct conversational evidence, such +as the mention that Amy owns a Cairn Terrier, yet +still produce incorrect final answers. In particular, +UniversalRAG demonstrates correct retrieval but +flawed reasoning, incorrectly concluding that the +dog in the image is not a Cairn Terrier despite vi- +sual similarity. This indicates that retrieval success +does not guarantee correct multimodal reasoning. +Textual memory models, on the other hand, of- +ten fail earlier due to missing or incomplete visual +grounding. Only MuRAG correctly integrates both +the retrieved textual clue and the visual evidence +to reach the correct conclusion. This case exposes +a critical gap in existing memory systems, namely +that multimodal reasoning requires not only retriev- +ing relevant information but also jointly reasoning +over visual and textual cues in a coherent manner. +Figure 18 focuses on knowledge resolution under +evolving conversational states. In this example, the +user explicitly corrects earlier information about +a preferred dog breed. The model must update its +memory and discard outdated knowledge. Most +evaluated methods struggle with this scenario. This +example reveals two distinct failure modes. First, +some memory systems fail to update their knowl- +edge, continuing to rely on outdated beliefs despite +the explicit correction in the dialogue. This be- +havior is particularly evident in some multimodal +memory methods, indicating that preserving mul- +timodal information alone does not guarantee ef- +fective knowledge revision. Second, even when +some textual memory models successfully incor- +porate the updated preference at the textual level, +they still fail to produce the correct answer due +to insufficient visual grounding. Lacking the abil- + Table 11: Benchmark performance results on Qwen-2.5-VL-7B with the retrieval number K=5. The best and +second-performed memory model(s) are highlighted with orange and blue backgrounds. +Qwen-2.5-VL-7B (K=5) Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2376 0.1075 0.5293 0.2216 0.2391 0.5928 0.5562 0.6045 0.2150 0.6291 0.6321 0.5536 0.5745 +BLEU-1 0.1865 0.0683 0.4531 0.1684 0.1903 0.5098 0.4668 0.5229 0.1626 0.5440 0.5464 0.4695 0.4870 +EM 0.0913 0.0228 0.2785 0.0685 0.0913 0.3288 0.2603 0.3288 0.0685 0.3516 0.3379 0.3105 0.2968 +LLM-Judge 0.2626 0.0662 0.7146 0.2580 0.2626 0.8539 0.7100 0.7877 0.2260 0.8105 0.8059 0.7123 0.7489 +VS +F1 0.1992 0.0398 0.6864 0.2416 0.1954 0.6239 0.7366 0.7614 0.1658 0.8664 0.8740 0.7847 0.8301 +BLEU-1 0.1873 0.0322 0.6307 0.2082 0.1840 0.5834 0.6596 0.7067 0.1473 0.8301 0.8387 0.7417 0.7731 +EM 0.1601 0.0131 0.4967 0.1634 0.1569 0.4118 0.5719 0.5817 0.1078 0.6928 0.6993 0.6340 0.6569 +LLM-Judge 0.1961 0.0343 0.6650 0.2288 0.1895 0.5964 0.7059 0.7418 0.1683 0.8725 0.8627 0.7778 0.8235 +TTL +F1 0.4500 0.3240 0.5938 0.4731 0.4486 0.2924 0.6882 0.5512 0.4147 0.8161 0.7853 0.7706 0.7631 +BLEU-1 0.3799 0.2557 0.5170 0.3980 0.3798 0.2295 0.6261 0.4759 0.3477 0.7464 0.7068 0.7027 0.6810 +EM 0.2374 0.0742 0.3858 0.2493 0.2374 0.1009 0.5134 0.3650 0.2107 0.6024 0.5846 0.6053 0.5519 +LLM-Judge 0.7092 0.6424 0.8353 0.7626 0.7033 0.7092 0.8220 0.7774 0.7107 0.9021 0.8620 0.8887 0.8754 +Reasoning +TR +F1 0.2545 0.1484 0.4088 0.2017 0.2553 0.5661 0.5235 0.5598 0.2294 0.5439 0.5233 0.5199 0.5779 +BLEU-1 0.2363 0.1305 0.3872 0.1836 0.2363 0.5326 0.4959 0.5306 0.2065 0.5211 0.4955 0.4952 0.5475 +EM 0.1545 0.0732 0.3089 0.1138 0.1545 0.3496 0.4065 0.3902 0.1382 0.4146 0.3740 0.3984 0.4309 +LLM-Judge 0.2805 0.1341 0.5325 0.2276 0.2764 0.8008 0.6626 0.7276 0.2480 0.6707 0.6870 0.6463 0.7317 +VR +F1 0.2552 0.0900 0.2711 0.1669 0.2594 0.4593 0.3963 0.4243 0.2015 0.4390 0.4205 0.3951 0.3950 +BLEU-1 0.2442 0.0758 0.2548 0.1536 0.2480 0.4459 0.3797 0.4108 0.1912 0.4175 0.3969 0.3767 0.3795 +EM 0.2011 0.0460 0.1552 0.0977 0.2011 0.3851 0.2989 0.3391 0.1609 0.3276 0.3046 0.3103 0.3103 +LLM-Judge 0.3046 0.1264 0.2931 0.1954 0.3046 0.6149 0.5000 0.5489 0.2586 0.5632 0.5632 0.4598 0.4885 +MR +F1 0.2411 0.1516 0.4056 0.2116 0.2428 0.4367 0.4503 0.4385 0.2101 0.4980 0.4954 0.4343 0.4756 +BLEU-1 0.1739 0.0982 0.3012 0.1497 0.1770 0.3347 0.3476 0.3355 0.1429 0.3915 0.3871 0.3295 0.3667 +EM 0.0340 0.0049 0.0631 0.0194 0.0340 0.0631 0.0777 0.0680 0.0194 0.0971 0.0971 0.0631 0.0874 +LLM-Judge 0.2985 0.1238 0.6917 0.2767 0.3058 0.8204 0.7379 0.7961 0.2791 0.8228 0.8204 0.7063 0.7888 +Knowledge +Management +KR +F1 0.2354 0.1805 0.3331 0.2429 0.2354 0.4292 0.3863 0.4686 0.2181 0.4176 0.4062 0.3835 0.3707 +BLEU-1 0.2005 0.1521 0.2798 0.2044 0.2000 0.3741 0.3305 0.4087 0.1883 0.3660 0.3606 0.3346 0.3152 +EM 0.1235 0.0988 0.1358 0.0988 0.1235 0.2099 0.1975 0.2222 0.1235 0.2099 0.1975 0.2099 0.1481 +LLM-Judge 0.3395 0.2160 0.5988 0.3210 0.3395 0.7407 0.5988 0.7593 0.2840 0.7469 0.6852 0.6358 0.6173 +CD +F1 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 +BLEU-1 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 +EM 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 +LLM-Judge 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 +AR +F1 0.9958 1.0000 0.9581 0.9891 0.9958 0.9849 0.9159 0.9785 0.9946 0.9532 0.9421 0.9749 0.9461 +BLEU-1 0.9953 1.0000 0.9575 0.9891 0.9953 0.9844 0.9141 0.9784 0.9946 0.9524 0.9413 0.9741 0.9459 +EM 0.9946 1.0000 0.9565 0.9891 0.9946 0.9837 0.9130 0.9783 0.9946 0.9511 0.9402 0.9728 0.9457 +LLM-Judge 0.9783 0.9837 0.9402 0.9728 0.9783 0.9674 0.9375 0.9620 0.9837 0.9429 0.9375 0.9565 0.9429 +Overall +F1 0.3625 0.2558 0.5478 0.3560 0.3619 0.5282 0.6038 0.6021 0.3354 0.6791 0.6688 0.6322 0.6457 +BLEU-1 0.3279 0.2255 0.4946 0.3164 0.3279 0.4792 0.5475 0.5483 0.2999 0.6288 0.6164 0.5817 0.5887 +EM 0.2519 0.1596 0.3711 0.2350 0.2507 0.3402 0.4296 0.4208 0.2279 0.4915 0.4798 0.4711 0.4611 +LLM-Judge 0.4331 0.3115 0.6715 0.4299 0.4307 0.7306 0.7119 0.7463 0.4129 0.7957 0.7823 0.7329 0.7586 +ity to accurately recognize or verify the dog breed +shown in the image, these models are unable to +align the updated textual knowledge with the vi- +sual evidence, leading to incorrect final predictions. +This case demonstrates that effective knowledge +resolution requires both explicit mechanisms for +memory revision and robust visual understanding +to ground updated knowledge in perceptual evi- +dence, which remains largely underexplored in cur- +rent multimodal designs. +A.8 Ethics Statement +A.8.1 Data Privacy +To ensure data privacy, the benchmark does not in- +clude any personally identifiable information from +real user conversations. All conversations were +either generated by LLMs or reconstructed from +open-sourced paper repositories, and were subse- +quently verified and edited under strong human +supervision by the author team. The majority of +images and portions of single-session dialogue data +were collected from open-source repositories. For +newly collected images sourced from the web, only +materials released under permissive or Creative +Commons licenses were used. All conversations +and question–answer pairs were reviewed by an- +notators to remove inaccurate, misleading, or in- +appropriate content and to avoid the inclusion of +sensitive material. The benchmark construction +process does not involve real user interactions or +private user data. Thus, the dataset does not involve +data privacy violations and is intended solely for +research and benchmarking purposes. +A.8.2 Annotation +All annotation and verification work in this bench- +mark was conducted exclusively for data quality +control and validation purposes. The annotation +tasks were limited to checking the correctness, con- +sistency, and completeness of conversational data +and model-generated outputs, including verifying +dialogue coherence across sessions, validating mul- +timodal grounding, and reviewing question–answer +pairs for clarity and correctness. Annotators were +treated solely as an interface for data verification +rather than as research subjects, and no analysis +of annotator behavior, agreement patterns, or in- +dividual decision strategies was performed. No + Table 12: Benchmark performance results on Qwen-2.5-VL-7B with the retrieval number K=15. The best and +second-performed memory model(s) are highlighted with orange and blue backgrounds. +Qwen-2.5-VL-7B (K=15) Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2376 0.1511 0.6123 0.2765 0.2391 0.5928 0.6258 0.6382 0.2150 0.6674 0.6626 0.6482 0.6182 +BLEU-1 0.1865 0.1071 0.5273 0.2124 0.1903 0.5098 0.5321 0.5462 0.1626 0.5808 0.5678 0.5698 0.5319 +EM 0.0913 0.0502 0.3151 0.0913 0.0913 0.3288 0.3105 0.3516 0.0685 0.3744 0.3379 0.3699 0.3288 +LLM-Judge 0.2626 0.1416 0.8082 0.3379 0.2626 0.8539 0.8037 0.8447 0.2260 0.8767 0.8721 0.8151 0.8174 +VS +F1 0.1992 0.0794 0.7812 0.3425 0.1954 0.6239 0.7905 0.8022 0.1658 0.8725 0.8452 0.8673 0.8607 +BLEU-1 0.1873 0.0680 0.7359 0.3032 0.1840 0.5834 0.7160 0.7337 0.1473 0.8362 0.8162 0.8295 0.8084 +EM 0.1601 0.0425 0.5752 0.2320 0.1569 0.4118 0.5915 0.6111 0.1078 0.6536 0.6471 0.6961 0.6569 +LLM-Judge 0.1961 0.0719 0.7696 0.3366 0.1895 0.5964 0.7484 0.7958 0.1683 0.8742 0.8448 0.8676 0.8497 +TTL +F1 0.4500 0.3410 0.6410 0.4981 0.4486 0.2924 0.5900 0.5302 0.4147 0.8071 0.7517 0.7782 0.7975 +BLEU-1 0.3799 0.2838 0.5694 0.4283 0.3798 0.2295 0.5096 0.4502 0.3477 0.7307 0.6785 0.7128 0.7291 +EM 0.2374 0.1662 0.4540 0.2908 0.2374 0.1009 0.3769 0.3264 0.2107 0.5964 0.5697 0.5994 0.6172 +LLM-Judge 0.7092 0.6810 0.8279 0.7611 0.7033 0.7092 0.7953 0.7582 0.7107 0.9050 0.8591 0.9050 0.9065 +Reasoning +TR +F1 0.2545 0.1930 0.4864 0.3335 0.2553 0.5661 0.5760 0.5614 0.2294 0.5798 0.5759 0.5752 0.5882 +BLEU-1 0.2363 0.1684 0.4539 0.3122 0.2363 0.5326 0.5485 0.5323 0.2065 0.5482 0.5413 0.5468 0.5602 +EM 0.1545 0.0976 0.3252 0.2276 0.1545 0.3496 0.4309 0.3902 0.1382 0.4228 0.4065 0.4472 0.4472 +LLM-Judge 0.2805 0.1870 0.6707 0.3943 0.2764 0.8008 0.7236 0.7358 0.2480 0.7724 0.8008 0.7358 0.7561 +VR +F1 0.2552 0.1155 0.3303 0.2119 0.2594 0.4593 0.4458 0.4676 0.2015 0.4678 0.4913 0.4697 0.3860 +BLEU-1 0.2442 0.0998 0.3144 0.2005 0.2480 0.4459 0.4335 0.4533 0.1912 0.4474 0.4712 0.4494 0.3713 +EM 0.2011 0.0690 0.2299 0.1379 0.2011 0.3851 0.3563 0.3678 0.1609 0.3563 0.3736 0.3621 0.2874 +LLM-Judge 0.3046 0.1494 0.4368 0.2644 0.3046 0.6149 0.5632 0.5862 0.2586 0.5690 0.5833 0.5632 0.4799 +MR +F1 0.2411 0.1747 0.4549 0.2471 0.2428 0.4367 0.4723 0.4547 0.2101 0.5042 0.5004 0.4841 0.4839 +BLEU-1 0.1739 0.1225 0.3372 0.1813 0.1770 0.3347 0.3698 0.3519 0.1429 0.3910 0.3840 0.3760 0.3748 +EM 0.0340 0.0146 0.0777 0.0243 0.0340 0.0631 0.0825 0.0825 0.0194 0.0922 0.0971 0.0874 0.0874 +LLM-Judge 0.2985 0.1893 0.8058 0.3811 0.3058 0.8204 0.7913 0.8301 0.2791 0.8568 0.8665 0.8010 0.8252 +Knowledge +Management +KR +F1 0.2354 0.1685 0.3740 0.2752 0.2354 0.4292 0.4470 0.4888 0.2181 0.4701 0.4629 0.4242 0.4175 +BLEU-1 0.2005 0.1290 0.3203 0.2338 0.2000 0.3741 0.3935 0.4345 0.1883 0.4166 0.4052 0.3738 0.3508 +EM 0.1235 0.0617 0.1605 0.1235 0.1235 0.2099 0.2222 0.2593 0.1235 0.2469 0.2222 0.2222 0.1975 +LLM-Judge 0.3395 0.2469 0.6296 0.3704 0.3395 0.7407 0.6605 0.7654 0.2840 0.7963 0.7716 0.7284 0.7099 +CD +F1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 +BLEU-1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 +EM 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 +LLM-Judge 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 +AR +F1 0.9958 0.9958 0.9513 0.9843 0.9958 0.9849 0.9224 0.9842 0.9946 0.9513 0.9523 0.9635 0.9464 +BLEU-1 0.9953 0.9953 0.9512 0.9840 0.9953 0.9844 0.9201 0.9840 0.9946 0.9512 0.9518 0.9629 0.9461 +EM 0.9946 0.9946 0.9511 0.9837 0.9946 0.9837 0.9185 0.9837 0.9946 0.9511 0.9511 0.9620 0.9457 +LLM-Judge 0.9783 0.9783 0.9375 0.9674 0.9783 0.9674 0.9429 0.9674 0.9837 0.9511 0.9348 0.9457 0.9375 +Overall +F1 0.3625 0.2794 0.6040 0.4035 0.3619 0.5282 0.6168 0.6182 0.3354 0.6925 0.6775 0.6788 0.6667 +BLEU-1 0.3279 0.2489 0.5503 0.3619 0.3279 0.4792 0.5574 0.5599 0.2999 0.6393 0.6244 0.6296 0.6129 +EM 0.2519 0.1894 0.4150 0.2700 0.2507 0.3402 0.4214 0.4290 0.2279 0.4915 0.4804 0.4997 0.4793 +LLM-Judge 0.4331 0.3504 0.7408 0.4906 0.4307 0.7306 0.7458 0.7694 0.4129 0.8209 0.8077 0.7969 0.7873 +personally identifiable information or demographic +attributes were collected or recorded during the an- +notation process. The annotation workflow did not +involve tracking individual annotators, comparing +annotator performance, or modeling human judg- +ment patterns. All annotation results were used +only to improve data quality and benchmark reli- +ability, with the research focus remaining only on +the properties of the data, models, and evaluation +framework rather than on any human behavior. All +annotators were fully informed that the annotated +data would be released publicly for research pur- +poses. Since all annotation and verification work +was conducted by the author team as part of the re- +search process, no external recruitment or monetary +compensation was involved. The benchmark study +does not involve any privacy data of real users or +human-subject research. +A.8.3 Scientific Artifacts +All data sources and models underlying the bench- +mark are used with explicit references and official +links. Detailed information can be found in Ap- +pendix A.2.2, A.4, and A.5. The benchmark primar- +ily targets English-language multimodal conversa- +tions across diverse daily-life and domain-specific +scenarios, as illustrated in Appendix A.2. No de- +mographic attributes of real individuals are repre- +sented or analyzed. The benchmark and dataset +will be released publicly with clear documentation, +permitting use for research purposes. +A.9 Potential Risks +This benchmark is designed for research and eval- +uation purposes only. While it does not involve +real user data or personal information, potential +risks may arise from unintended misuse, such as +over-interpreting benchmark results as indicators +of real-world deployment readiness. In addition, +models evaluated on this benchmark may inherit +biases present in underlying pretrained language +model backbones (Schramowski et al., 2022). We +mitigate these risks by clearly scoping the bench- +mark to controlled memory research settings and +by providing transparent documentation of data +sources, evaluation protocols, and limitations. + Table 13: Benchmark performance results on Qwen-2.5-VL-7B with the retrieval number K=20. The best and +second-performed memory model(s) are highlighted with orange and blue backgrounds. +Qwen-2.5-VL-7B (K=20) Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS +Extract. +& +Adapt. +FR +F1 0.2376 0.1743 0.6303 0.3020 0.2391 0.5928 0.6145 0.6265 0.2150 0.6596 0.6609 0.6589 0.6216 +BLEU-1 0.1865 0.1295 0.5438 0.2366 0.1903 0.5098 0.5210 0.5396 0.1626 0.5656 0.5639 0.5813 0.5335 +EM 0.0913 0.0548 0.3242 0.1142 0.0913 0.3288 0.2922 0.3379 0.0685 0.3516 0.3425 0.3790 0.3379 +LLM-Judge 0.2626 0.1598 0.8174 0.3744 0.2626 0.8539 0.8174 0.8288 0.2260 0.8950 0.8790 0.8402 0.8128 +VS +F1 0.1992 0.0953 0.8144 0.3567 0.1954 0.6239 0.7782 0.7963 0.1658 0.8676 0.8444 0.8583 0.8647 +BLEU-1 0.1873 0.0849 0.7704 0.3187 0.1840 0.5834 0.7026 0.7300 0.1473 0.8341 0.8160 0.8198 0.8230 +EM 0.1601 0.0588 0.5980 0.2386 0.1569 0.4118 0.5882 0.6078 0.1078 0.6503 0.6503 0.6765 0.6569 +LLM-Judge 0.1961 0.0850 0.8023 0.3546 0.1895 0.5964 0.7337 0.7925 0.1683 0.8856 0.8611 0.8660 0.8611 +TTL +F1 0.4500 0.3789 0.6104 0.4900 0.4486 0.2924 0.5542 0.5353 0.4147 0.7841 0.7211 0.7911 0.7859 +BLEU-1 0.3799 0.3180 0.5355 0.4137 0.3798 0.2295 0.4735 0.4566 0.3477 0.7122 0.6478 0.7250 0.7184 +EM 0.2374 0.1899 0.4243 0.2849 0.2374 0.1009 0.3561 0.3442 0.2107 0.5905 0.5312 0.6142 0.6172 +LLM-Judge 0.7092 0.6929 0.8131 0.7448 0.7033 0.7092 0.7611 0.7760 0.7107 0.8961 0.8694 0.8976 0.9021 +Reasoning +TR +F1 0.2545 0.1858 0.5154 0.3484 0.2553 0.5661 0.5502 0.5941 0.2294 0.5642 0.5613 0.5445 0.5890 +BLEU-1 0.2363 0.1652 0.4848 0.3255 0.2363 0.5326 0.5220 0.5679 0.2065 0.5345 0.5291 0.5180 0.5615 +EM 0.1545 0.0976 0.3577 0.2358 0.1545 0.3496 0.3821 0.4472 0.1382 0.3984 0.3984 0.4146 0.4390 +LLM-Judge 0.2805 0.1870 0.7073 0.4268 0.2764 0.8008 0.7073 0.7398 0.2480 0.7967 0.7846 0.7073 0.7642 +VR +F1 0.2552 0.1383 0.3654 0.2171 0.2594 0.4593 0.4216 0.4410 0.2015 0.5320 0.4344 0.4772 0.3695 +BLEU-1 0.2442 0.1208 0.3507 0.2016 0.2480 0.4459 0.4096 0.4304 0.1912 0.5130 0.4192 0.4563 0.3550 +EM 0.2011 0.0862 0.2644 0.1264 0.2011 0.3851 0.3391 0.3621 0.1609 0.4138 0.3333 0.3678 0.2644 +LLM-Judge 0.3046 0.1954 0.4770 0.2672 0.3046 0.6149 0.5517 0.5891 0.2586 0.6322 0.5460 0.5718 0.4483 +MR +F1 0.2411 0.1837 0.4678 0.2616 0.2428 0.4367 0.4732 0.4554 0.2101 0.4988 0.4955 0.4802 0.4742 +BLEU-1 0.1739 0.1271 0.3523 0.1932 0.1770 0.3347 0.3656 0.3550 0.1429 0.3863 0.3757 0.3664 0.3683 +EM 0.0340 0.0194 0.0922 0.0291 0.0340 0.0631 0.0874 0.0777 0.0194 0.0874 0.0874 0.0825 0.0971 +LLM-Judge 0.2985 0.2039 0.8107 0.4029 0.3058 0.8204 0.7913 0.8374 0.2791 0.8786 0.8689 0.8107 0.8155 +Knowledge +Management +KR +F1 0.2354 0.1924 0.4200 0.2909 0.2354 0.4292 0.4290 0.4597 0.2181 0.4648 0.4604 0.4354 0.3967 +BLEU-1 0.2005 0.1546 0.3666 0.2530 0.2000 0.3741 0.3657 0.4083 0.1883 0.4060 0.3969 0.3858 0.3343 +EM 0.1235 0.0741 0.2099 0.1481 0.1235 0.2099 0.1852 0.2222 0.1235 0.2346 0.1975 0.2593 0.1728 +LLM-Judge 0.3395 0.2593 0.7037 0.3951 0.3395 0.7407 0.7099 0.7531 0.2840 0.8025 0.7778 0.7593 0.6975 +CD +F1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 +BLEU-1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 +EM 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 +LLM-Judge 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 +AR +F1 0.9958 0.9956 0.9514 0.9840 0.9958 0.9849 0.9233 0.9677 0.9946 0.9307 0.9322 0.9471 0.9519 +BLEU-1 0.9953 0.9952 0.9512 0.9838 0.9953 0.9844 0.9210 0.9676 0.9946 0.9297 0.9306 0.9466 0.9515 +EM 0.9946 0.9946 0.9511 0.9837 0.9946 0.9837 0.9185 0.9674 0.9946 0.9293 0.9293 0.9457 0.9511 +LLM-Judge 0.9783 0.9783 0.9348 0.9674 0.9783 0.9674 0.9457 0.9565 0.9837 0.9266 0.9239 0.9348 0.9429 +Overall +F1 0.3625 0.2966 0.6157 0.4118 0.3619 0.5282 0.6018 0.6132 0.3354 0.6884 0.6602 0.6785 0.6629 +BLEU-1 0.3279 0.2651 0.5618 0.3683 0.3279 0.4792 0.5409 0.5572 0.2999 0.6357 0.6069 0.6286 0.6117 +EM 0.2519 0.2005 0.4243 0.2741 0.2507 0.3402 0.4085 0.4296 0.2279 0.4874 0.4635 0.4985 0.4787 +LLM-Judge 0.4331 0.3644 0.7554 0.5018 0.4307 0.7306 0.7390 0.7700 0.4129 0.8320 0.8068 0.7992 0.7846 +A.10 Use of AI Assistants +LLMs are used in this work strictly as auxiliary +tools for data construction, an evaluation metric, +and limited language polishing. During dataset +construction, LLMs assist in drafting candidate +multi-session conversational texts under predefined +structural constraints and scenario specifications. +These drafts are subsequently reviewed, edited, and +refined by annotators, who also insert appropriate +images and ensure that multimodal dependencies +are genuine, necessary, and non-trivial for down- +stream tasks. LLMs are further used to generate +part of the candidate question–answer pairs and +to perform preliminary checks for coherence, clar- +ity, and factual consistency. All QA pairs are then +manually verified and revised by annotators. In +addition, LLMs are used in a limited manner for +language polishing of the manuscript, including +improving fluency and presentation. In our eval- +uation, we also adopt LLM-as-a-Judge as one of +the evaluation metrics, following common practice +in prior work (Li et al., 2025). All technical con- +tent, experimental analysis, and scientific claims +are authored and finalized by the authors. + Table 14: Retrieval evaluation results on Qwen-2.5-VL-7B with different retrieval number K. Since answer refusal +(AR) questions intentionally introduce incorrect information and contain no supporting evidence in the conversation, +evaluation metrics for this category are not supported. +Qwen-2.5-VL-7B +Recall@K Precision@K Hit@K +5 10 15 20 5 10 15 20 5 10 15 20 +Overall +FIFO 0.0116 0.0381 0.0685 0.0950 0.0043 0.0087 0.0123 0.0129 0.0196 0.0491 0.0792 0.1081 +Gen. Agent 0.1707 0.2385 0.2854 0.3153 0.0955 0.0684 0.0544 0.0453 0.2718 0.3491 0.3962 0.4257 +NaiveRAG 0.5381 0.6723 0.7420 0.7877 0.2694 0.1757 0.1315 0.1047 0.7236 0.8179 0.8605 0.8900 +MuRAG 0.7506 0.8601 0.8990 0.9228 0.3686 0.2220 0.1572 0.1220 0.8861 0.9384 0.9548 0.9666 +UniversalRAG 0.7311 0.8411 0.8781 0.8998 0.3691 0.2206 0.1555 0.1204 0.8697 0.9227 0.9404 0.9496 +NGM 0.6192 0.7475 0.7892 0.8065 0.3564 0.3457 0.3450 0.3424 0.7708 0.8612 0.8874 0.9037 +AUGUSTUS 0.6729 0.7529 0.7785 0.7860 0.3341 0.2488 0.2213 0.2124 0.8553 0.8893 0.8959 0.8978 +FR +FIFO 0.0006 0.0354 0.0514 0.0793 0.0009 0.0059 0.0064 0.0075 0.0046 0.0365 0.0548 0.0822 +Gen. Agent 0.1027 0.1363 0.1708 0.2062 0.0320 0.0237 0.0225 0.0215 0.1370 0.1872 0.2420 0.2922 +NaiveRAG 0.4802 0.6297 0.7379 0.7811 0.2082 0.1511 0.1239 0.1014 0.6849 0.8265 0.8813 0.9041 +MuRAG 0.6248 0.7712 0.8228 0.8647 0.2630 0.1840 0.1397 0.1146 0.7991 0.9041 0.9178 0.9406 +UniversalRAG 0.6387 0.7940 0.8328 0.8768 0.2676 0.1913 0.1431 0.1180 0.8265 0.9178 0.9269 0.9498 +NGM 0.5025 0.6557 0.6999 0.7102 0.2348 0.2311 0.2329 0.2289 0.6712 0.8037 0.8265 0.8447 +AUGUSTUS 0.5766 0.6626 0.6771 0.6862 0.2428 0.2025 0.1779 0.1748 0.7717 0.8174 0.8219 0.8402 +VS +FIFO 0.0172 0.0485 0.0678 0.0842 0.0059 0.0082 0.0085 0.0074 0.0261 0.0556 0.0719 0.0882 +Gen. Agent 0.2173 0.2801 0.3309 0.3505 0.0706 0.0467 0.0379 0.0312 0.2843 0.3660 0.4183 0.4444 +NaiveRAG 0.6723 0.7979 0.8300 0.8724 0.2366 0.1474 0.1037 0.0815 0.7712 0.8693 0.8856 0.9216 +MuRAG 0.9080 0.9534 0.9660 0.9774 0.3379 0.1827 0.1237 0.0935 0.9444 0.9673 0.9739 0.9869 +UniversalRAG 0.8906 0.9438 0.9570 0.9651 0.3301 0.1801 0.1218 0.0918 0.9444 0.9739 0.9837 0.9902 +NGM 0.7889 0.8799 0.8982 0.9217 0.3445 0.3204 0.3134 0.3058 0.8562 0.9183 0.9346 0.9542 +AUGUSTUS 0.8363 0.8872 0.9056 0.9081 0.3124 0.2284 0.2062 0.1960 0.9248 0.9510 0.9542 0.9510 +TTL +FIFO 0.0026 0.0263 0.0938 0.1358 0.0042 0.0128 0.0265 0.0288 0.0208 0.0504 0.1246 0.1721 +Gen. Agent 0.3152 0.4425 0.5036 0.5488 0.2570 0.1831 0.1375 0.1119 0.5460 0.6528 0.7003 0.7240 +NaiveRAG 0.6837 0.8412 0.9017 0.9234 0.5501 0.3436 0.2457 0.1884 0.9496 0.9703 0.9881 0.9911 +MuRAG 0.8523 0.9566 0.9760 0.9785 0.6754 0.3932 0.2671 0.2007 0.9822 0.9852 0.9941 0.9970 +UniversalRAG 0.8617 0.9583 0.9682 0.9787 0.6967 0.3970 0.2669 0.2018 0.9763 0.9822 0.9881 0.9941 +NGM 0.7617 0.8919 0.9194 0.9203 0.7029 0.6919 0.6934 0.6930 0.9407 0.9763 0.9822 0.9822 +AUGUSTUS 0.7231 0.8508 0.8903 0.9088 0.5569 0.3690 0.3094 0.2914 0.9733 0.9822 0.9881 0.9911 +TR +FIFO 0.0081 0.0122 0.0325 0.0569 0.0033 0.0024 0.0033 0.0037 0.0081 0.0163 0.0325 0.0569 +Gen. Agent 0.0447 0.1043 0.1301 0.1558 0.0130 0.0154 0.0130 0.0122 0.0650 0.1463 0.1707 0.2033 +NaiveRAG 0.3828 0.5000 0.5413 0.6009 0.1301 0.0870 0.0640 0.0524 0.5610 0.6585 0.6829 0.7317 +MuRAG 0.6640 0.7724 0.8266 0.8557 0.2163 0.1285 0.0927 0.0732 0.8455 0.8943 0.9187 0.9187 +UniversalRAG 0.6938 0.8089 0.8652 0.8733 0.2293 0.1341 0.0970 0.0736 0.8618 0.9106 0.9350 0.9431 +NGM 0.4898 0.5928 0.6179 0.6321 0.1846 0.1688 0.1626 0.1609 0.6504 0.7561 0.7805 0.8049 +AUGUSTUS 0.6497 0.7243 0.7595 0.7683 0.2280 0.1743 0.1601 0.1509 0.8455 0.8780 0.8780 0.8780 +VR +FIFO 0.0000 0.0235 0.0551 0.0893 0.0000 0.0075 0.0126 0.0155 0.0000 0.0287 0.0632 0.1092 +Gen. Agent 0.1439 0.1888 0.2521 0.2817 0.0885 0.0621 0.0544 0.0448 0.2874 0.3391 0.4138 0.4310 +NaiveRAG 0.3093 0.4774 0.6010 0.6632 0.1540 0.1264 0.1115 0.0948 0.5057 0.6782 0.7759 0.8333 +MuRAG 0.6158 0.7774 0.8551 0.8916 0.3299 0.2236 0.1701 0.1353 0.8276 0.9080 0.9483 0.9598 +UniversalRAG 0.5497 0.6978 0.7560 0.7979 0.3356 0.2149 0.1586 0.1259 0.7759 0.8333 0.8621 0.8851 +NGM 0.4803 0.6351 0.7183 0.7541 0.2603 0.2707 0.2761 0.2762 0.6839 0.7989 0.8506 0.8793 +AUGUSTUS 0.5110 0.5722 0.5796 0.5924 0.3430 0.3139 0.3032 0.3045 0.7644 0.8103 0.8103 0.8218 +MR +FIFO 0.0178 0.0591 0.0736 0.0914 0.0058 0.0117 0.0097 0.0085 0.0243 0.0728 0.0777 0.0971 +Gen. Agent 0.1219 0.1752 0.2122 0.2376 0.0505 0.0359 0.0298 0.0252 0.2039 0.2621 0.2913 0.3204 +NaiveRAG 0.5659 0.6600 0.7306 0.7793 0.2010 0.1257 0.0958 0.0784 0.7379 0.8252 0.8738 0.8981 +MuRAG 0.7626 0.8527 0.9034 0.9237 0.2641 0.1597 0.1175 0.0925 0.9175 0.9612 0.9806 0.9806 +UniversalRAG 0.7506 0.8590 0.9108 0.9364 0.2612 0.1631 0.1204 0.0949 0.8981 0.9563 0.9806 0.9854 +NGM 0.6023 0.7464 0.7927 0.8133 0.2551 0.2415 0.2401 0.2394 0.7573 0.8835 0.9223 0.9369 +AUGUSTUS 0.7162 0.7748 0.8048 0.8014 0.2596 0.1964 0.1783 0.1684 0.8786 0.9175 0.9320 0.9223 +KR +FIFO 0.0412 0.0607 0.0885 0.1091 0.0099 0.0086 0.0099 0.0099 0.0494 0.0864 0.1111 0.1235 +Gen. Agent 0.0782 0.1091 0.1214 0.1389 0.0247 0.0185 0.0148 0.0136 0.1111 0.1728 0.1852 0.2346 +NaiveRAG 0.3899 0.5300 0.5808 0.6529 0.1605 0.1111 0.0823 0.0685 0.5679 0.7160 0.7531 0.8025 +MuRAG 0.5752 0.7495 0.8015 0.8424 0.2272 0.1506 0.1119 0.0895 0.7531 0.8889 0.9012 0.9259 +UniversalRAG 0.4750 0.6453 0.7433 0.7841 0.1852 0.1259 0.1029 0.0827 0.6790 0.8272 0.8889 0.8889 +NGM 0.4141 0.5599 0.6626 0.6863 0.1934 0.1841 0.1914 0.1907 0.6049 0.7037 0.7778 0.8025 +AUGUSTUS 0.4483 0.5523 0.5860 0.5970 0.1895 0.1480 0.1324 0.1287 0.6420 0.7160 0.7284 0.7284 +CD +FIFO 0.0432 0.0494 0.0617 0.0741 0.0099 0.0062 0.0049 0.0043 0.0494 0.0494 0.0617 0.0741 +Gen. Agent 0.0432 0.1091 0.1728 0.1934 0.0148 0.0198 0.0230 0.0204 0.0617 0.1852 0.2469 0.2593 +NaiveRAG 0.3868 0.4650 0.5539 0.6284 0.1333 0.0827 0.0667 0.0574 0.5432 0.5926 0.7037 0.7407 +MuRAG 0.6383 0.7868 0.8218 0.8881 0.2148 0.1296 0.0897 0.0759 0.7407 0.8519 0.8765 0.9259 +UniversalRAG 0.4877 0.6008 0.6605 0.6687 0.1704 0.1037 0.0765 0.0586 0.5926 0.7160 0.7407 0.7284 +NGM 0.4428 0.5621 0.6074 0.6383 0.1774 0.1673 0.1642 0.1596 0.5802 0.7160 0.7407 0.7654 +AUGUSTUS 0.6049 0.6584 0.6893 0.6770 0.2123 0.1578 0.1446 0.1385 0.6914 0.7531 0.7778 0.7654 + Cap-Sleeved Midi Dresses. +dress. +dress. +Midi Dress. +UniversalRAG +According to the conversation, what category is the clothing in the picture? +Relevant clue snippets in the conversation history +NaiveRAG +The clothing in the picture is +a fitted, knee-length dress. +A-Mem MemoryOS +Great! Let me start with the Cap-Sleeved Midi Dresses. Here’s the first one. +Lin +Oh wow, the moment I opened this one, the shine caught my eye immediately. Let me +take in the details. +Assistant +What do you think of this dress? +Lin This dress has such a striking metallic sheen—like it was made for evening lights. The +bronze tones and scalloped stripes feel glamorous but still structured. It’s definitely a +confidence-boosting piece. +Assistant +MuRAG AUGUSTUS +Cap-Sleeved Midi Dress. +Here's the second dress. +Lin +Aww, this one immediately gives off a softer energy. The floral pattern feels calming in +contrast to the first one. +Assistant +Insufficient visual +& adaptation ability. +More conversation on Cap-Sleeved Midi Dresses with more visual examples… +Insufficient +adaptation ability. +Insufficient visual +& adaptation ability. +Insufficient visual +& adaptation ability. +Figure 16: Case study with the test-time learning example. + No. +UniversalRAG +Yes, the dog in the +picture is the same +breed as Amy’s. +No. The dog in the picture is a +Cairn Terrier, while Lena's +friend Amy has a Cairn Terrier. +The dog in the picture is not +the same breed as Amy's pet. +Is the dog in the picture the same breed as the pet of any friend or classmate mentioned by Lena? +Answer Yes or No. If yes, then please specify which person's pet it shares the same breed with. +Relevant clue snippets in the conversation history +NaiveRAG +No. +A-Mem +No. +MemoryOS +Not yet! I want something that fits her personality — gentle but lively. I’ve been asking +my friends for ideas too. My friend Amy has a Cairn Terrier, and she gave me some tips. +Lena +Oh, Cairn Terriers! They’re quite different from Maltese dogs: more energetic and +adventurous. Amy’s must keep her busy! What did she say about naming pets? +Assistant +Amy said her dog is super active and always digging around in the yard. Apparently, that’s +part of their nature. They were bred to hunt small animals in rocky areas! +Lena Exactly! Cairn Terriers are brave and curious little dogs. They love to explore and have +tons of energy. They can be stubborn at times, but that’s just because they’re so +independent. Did Amy mention if it’s hard to train her dog? +Assistant +… +MuRAG +AUGUSTUS +No. +Insufficient +retrieval. +Correct retrieved clues, +but incorrect reasoning. +Othe dialogs about Lucy’s and Lena’s classmate’s dog… +Insufficient +retrieval. +Insufficient +retrieval. +Insufficient +retrieval. +Figure 17: Case study with the visual-centric reasoning example. + No. +No, the dog in the picture is +not the breed that Lena's +sister likes. Lena's sister +prefers Standard Schnauzers. +UniversalRAG +No, the dog in the picture is not +the breed that Lena's sister +likes. Lena's sister actually loves +Standard Poodles. +Is the dog in the picture the breed that Lena's sister likes? +Relevant clue snippets in the conversation history +NaiveRAG +No. +A-Mem +No, the dog in the picture is not the +breed that Lena's sister likes. +Lena's sister prefers Standard +Schnauzers over Standard Poodles. +MemoryOS +My sister actually loves Standard Poodles. She says their elegance and athletic build +make them her dream dog. +Lena +That totally makes sense! People who appreciate both intelligence and style often adore +Standard Poodles. They’re such graceful companions and do so well in active families. +Assistant +I think I’d love spending time with a Miniature Poodle. They seem like the perfect +balance: smart, active, but still small enough for apartment life. +Lena +You’re absolutely right, Lena. Miniature Poodles often fit well into both active and cozy +lifestyles. They enjoy long walks and puzzles but also love curling up beside their owners. +Given your interest in animal behavior, you’d probably find their problem-solving +instincts fascinating. +Assistant +MuRAG +AUGUSTUS +No. +Other dialogs… +Oh wait, I just realized I got it wrong earlier. My sister doesn’t actually like Standard +Poodles. Her favorite breed is the Standard Schnauzer, as shown in the image! +Lena Ah, that explains it! Standard Schnauzers have a totally different charm — confident, +spirited, and with those adorable beards. They’re smart and loyal too, just in their own +unique way. +Assistant +… +Lack knowledge +update ability. +Insufficient +visual ability. +Insufficient +visual ability. +Insufficient +visual ability. +Lack knowledge +update ability. +Lack knowledge +update ability. +Figure 18: Case study with the knowledge resolution example. + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/paper.txt b/wizard-work/sphinx/archive-memgallery-2026-10-21/paper.txt new file mode 100644 index 0000000..657216f --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/paper.txt @@ -0,0 +1,2458 @@ + Mem-Gallery: Benchmarking Multimodal Long-Term Conversational + Memory for MLLM Agents + + Yuanchen Bei1 , Tianxin Wei1 , Xuying Ning1 , Yanjun Zhao1 , Zhining Liu1 , + Xiao Lin1 , Yada Zhu2 , Hendrik Hamann3,4 , Jingrui He1 , Hanghang Tong1 + 1 + University of Illinois Urbana-Champaign 2 MIT-IBM Watson AI Lab, IBM Research + 3 + Stony Brook University 4 Brookhaven National Laboratory + + Timeline + … Multimodal Multi-Session … + 2025-03-02 2025-06-22 + Conversation + Abstract Session-1 + After Multi-Session + Conversations… + Session-n + We finally had Luna’s second + ... I met the sweetest Cocker + Spaniel today! … session today! … She was more + cooperative this time. … + Attached Image + Long-term memory is a critical capability for Attached Image + + Typical Springer energy! … +arXiv:2601.03515v1 [cs.CL] 7 Jan 2026 + + + + + That’s beautiful. They’re Example Topics: + Familiarity often stabilizes + multimodal large language model (MLLM) incredibly empathetic dogs. … + Dog, Behavior, + behavioral responses. … + + Academic, Life + agents, particularly in conversational settings Multi-Round Dialogues … Multi-Round Dialogues … + + where information accumulates and evolves …His name is Milo. I think I’m + falling in love with spaniels… + I want to merge Milo’s video + too. It adds emotional + expression variety. … + over time. However, existing benchmarks ei- … Once someone meets a + Attached Image + Cocker Spaniel, it’s hard not + Smart idea! Cocker data will + ther evaluate multi-session memory in text- to be enchanted! + Attached Image + enrich the affective + dimension of your study. + + only conversations or assess multimodal un- + Multi-Dimension Evaluation + derstanding within localized contexts, failing + Memory Extraction & Adaptation + to evaluate how multimodal memory is pre- Factual Retrieval Visual-centric Search Test-Time Learning + + served, organized, and evolved across long- What candidate names Find the two images related + What breed is the dog + have been listed when to Lucy‘s pet. Return the + in the attached image? + term conversational trajectories. Thus, we intro- naming Lena's dog? image_id(s). + + + + duce Mem-Gallery1 , a new benchmark for eval- … Coral... Sunny… or Lumi … + + … + … Cocker Spaniel: + + Others: + … + + … + … + uating multimodal long-term conversational + Memory Reasoning + memory in MLLM agents. Mem-Gallery fea- + Temporal Reasoning Visual-centric Reasoning Multi-entity Reasoning + tures high-quality multi-session conversations + When did Lena and Lumi meet The dog in the picture more How did Luna and Rocky respond + grounded in both visual and textual informa- the Papillon at the park? similar to who’s pet? differently when a clipboard was + dropped during the lab session? + + + tion, with long interaction horizons and rich Date: 2024-09-28 + I took Lumi to the riverside park + Lena’s + … + Amy’s + … + … When … a clipboard, he flinched + but then wagged his tail and … + … + today. we met … little dog. + Lucy’s Other’s … Luna, on the other hand, barked + multimodal dependencies. Building on this … … … once and … … + + + dataset, we propose a systematic evaluation Memory Knowledge Management + + framework that assesses key memory capabil- Knowledge Resolution Conflict Detection Answer Refusal + + + ities along three functional dimensions: mem- After Lena’s correction, which + dog breed does her sister + Conflict with the memory or not: + Sussex Spaniel has white-and- + What is the name of Lena's + friend Amy's cat? + actually like? liver coat pattern and alert eyes? + ory extraction and test-time adaptation, mem- My sister loves Standard Poodles. ... Sussex spaniel… + Amy has a Cairn Terrier… she + told me she named her Cairn + I got it wrong earlier. Her favorite … teddy bear-short fur… Terrier ‘Pebble’… … + ory reasoning, and memory knowledge man- breed is as the image. … + … the Standard Schnauzer … … + Not mentioned + + agement. Extensive benchmarking across thir- + teen memory systems reveals several key find- + Figure 1: The conceptual illustration of Mem-Gallery. + ings, highlighting the necessity of explicit mul- + timodal information retention and memory or- + a primary medium through which agents acquire, + ganization, the persistent limitations in memory + reasoning and knowledge management, as well refine, and utilize memory, making conversational + as the efficiency bottleneck of current models. settings a natural and critical testbed for evaluating + long-term memory capabilities (Wu et al., 2025). + 1 Introduction Human memory in conversation is inherently + multimodal and evolving (Ardesch et al., 2019; + The rapid progress of Multimodal Large Language + Luppi et al., 2022). An effective agent therefore re- + Models (MLLMs) has enabled the development of + quires the ability to not only recall past information + agents that can perceive, reason, and interact with + but also integrate visual and textual cues, reason + the world through both language and vision (Wu + across events, and update outdated knowledge as + et al., 2023; Zhang et al., 2024a). A fundamental + conversations progress (Hu et al., 2025b; Bo et al., + capability for such agents is long-term memory: + 2025). However, endowing MLLM agents with + store, retrieve, and update information accumulated + such long-term multimodal conversational memory + over extended interactions (Zhang et al., 2025b). In + largely remains an open challenge, and its system- + particular, multi-session conversations constitute + atic evaluation is still underexplored. + 1 + https://github.com/YuanchenBei/Mem-Gallery Despite growing interest in agentic memory, ex- + isting benchmarks reflect a fundamental mismatch visual information in memory is beneficial. (2) +with the real-world conversational memory for Memory Organization Importance: highlight- +MLLM agents. Current conversation benchmarks ing the necessity of principled multimodal mem- +tend to fall into two disjoint categories. On one ory organization and maintenance. (3) Memory +hand, text-only conversational memory bench- Reasoning and Knowledge Management Limita- +marks evaluate memory over multi-session dia- tions: existing multimodal memory models strug- +logues but discard visual modality (Wu et al., 2025; gle in reasoning-intensive settings, as well as in +Hu et al., 2025a). On the other hand, localized handling knowledge updates and conflicts. (4) Ef- +multimodal context understanding benchmarks ficiency Bottleneck: multimodal memory overall +introduce the visual modality but typically focus introduces larger storage and retrieval overhead +on short-horizon understanding within only one that may hinder practical deployment. +session, failing to assess cross-session information Our contributions are summarized as follows: +evolution and management (Liu et al., 2024; Xue + • New Scenario & Dataset: We formulate multi- +et al., 2025). This limitation makes them unsuit- + modal long-term conversational memory as an +able for memory evaluation. Consequently, current + evolving system that spans multiple sessions, +conversational benchmarks still fall short in evalu- + modalities, and memory functions, and build a +ating how agents organize, maintain, and retrieve + customized conversational dataset. +multimodal memory over extended conversational +timelines, where visual and linguistic information • Evaluation Framework: We propose a new eval- +interact dynamically. uation framework that systematically assesses + To address this gap, we propose Mem-Gallery, multimodal long-term conversational memory +a benchmark designed to evaluate multimodal long- across memory extraction & adaptation, reason- +term conversational memory in MLLM agents sys- ing, and knowledge management. +tematically, as described in Figure 1. Mem-Gallery • Benchmark Takeaways: Through extensive +firstly introduces a new dataset of multi-session benchmarking, we reveal key advantages and +conversations grounded in both images and text, limitations of existing memory designs in multi- +reflecting daily and domain-specific knowledge. modal long-term conversations, providing action- +Each conversation spans a long interaction horizon, able insights for future research. +where information is incrementally introduced, ref- +erenced, and updated. Based on this dataset, we 2 Related Works +structure evaluation tasks into three key functional +dimensions of memory. These three dimensions 2.1 Multi-Round Conversational Benchmark +correspond to the core stages of long-term conver- A number of dialogue benchmarks have been pro- +sational memory in real-world agents: acquiring us- posed in recent years that can be used to evaluate +able memory, reasoning over evolving multimodal memory capabilities, such as LoCoMo (Maharana +evidence, and regulating memory under dynamic et al., 2024), LongMemEval (Wu et al., 2025), and +and potentially inconsistent states. Specifically, MemoryAgentBench (Hu et al., 2025a). However, +Mem-Gallery evaluates: (1) Memory Extraction most of these benchmarks are text-only and do not +and Adaptation, which includes multimodal fac- provide an evaluation of multimodal capabilities. +tual retrieval, visual-centric search, and test-time Although several localized multimodal dialogue +learning over long multimodal histories. (2) Mem- benchmarks have been introduced recently, like +ory Reasoning, which evaluates how agents con- MMDU (Liu et al., 2024) and MMRC (Xue et al., +duct reasoning over multimodal memory clues, 2025), they are single-session only and lack the +including temporal reasoning, visual-centric rea- multi-session conversation structure. Therefore, +soning, and multi-entity reasoning. (3) Memory they are used to assess multi-round context under- +Knowledge Management, which examines the standing abilities rather than long-term memory. +ability to resolve knowledge contradictions, detect We compare Mem-Gallery with representative +conflicts, and appropriately refuse to answer when related works in Table 1. Overall, prior benchmarks +information is missing, outdated, or inconsistent. exhibit a structural misalignment with multimodal + Benchmarking across thirteen memory mecha- long-term memory evaluation, either overlooking +nisms reveals several findings. (1) Multimodal visual information, lacking multi-session structure, +Information Effectiveness: explicitly preserving or failing to support the assessment of multimodal + Table 1: Comparison between Mem-Gallery with representative conversational benchmarks. ✓: Satisfies; ✗: Does + ✗ : Text modality only. +not satisfy; ✓ + Conversational Characteristics Extract.&Adapt. Reasoning Management + Benchmark + A. Round A. Img. Multi-Sess. MM Info. FR VS TTL TR VR MR KR CD AR + DuLeMon (Xu et al., 2022) 8.16 — ✓ ✗ ✗ + ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ + DialogBench (Ou et al., 2024) 7.48 — ✓ ✗ ✗ + ✓ ✗ ✗ ✗ ✗ ✗ + ✓ ✗ + ✓ ✗ ✗ + MemoryBank (Zhong et al., 2024) 3.77 — ✓ ✗ ✗ + ✓ ✗ ✗ ✗ + ✓ ✗ ✗ ✗ ✗ ✗ + MMDU (Liu et al., 2024) 14.95 3.83 ✗ ✓ ✓ ✗ ✗ ✗ ✓ ✓ ✗ ✗ ✗ + LoCoMo (Maharana et al., 2024) 10.81 3.35 ✓ ✓ ✓ ✗ ✗ ✗ + ✓ ✗ ✓ ✗ ✗ ✗ + ✓ + LOCCO (Jia et al., 2025) 4.77 — ✓ ✗ ✗ + ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ + LongMemEval (Wu et al., 2025) 5.19 — ✓ ✗ ✗ + ✓ ✗ ✗ ✗ + ✓ ✗ ✗ + ✓ ✗ + ✓ ✗ ✗ + ✓ + MemoryAgentBench (Hu et al., 2025a) 9.55 — ✓ ✗ ✗ + ✓ ✗ ✗ + ✓ ✗ + ✓ ✗ ✗ + ✓ ✗ + ✓ ✗ ✗ + MMRC (Xue et al., 2025) 12.90 2.90 ✗ ✓ ✓ ✗ ✗ ✓ ✓ ✓ ✗ + ✓ ✗ ✓ + Mem-Gallery (Ours) 16.51 4.18 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ +* A. Round: average user-assistant dialogue round numbers per session. A. Img.: average image numbers per session. Multi-Sess. & MM Info.: whether the dataset +has basic characteristics of multi-session and multimodal information. This property serves as a fundamental prerequisite for multimodal long-term conversational +memory. For the three evaluation dimensions: (i) Memory extraction & adaptation includes factual retrieval (FR), visual-centric search (VS), and test-time learning +(TTL) subtasks. (ii) Memory reasoning includes temporal reasoning (TR), visual-centric reasoning (VR), and multi-entity reasoning (MR) subtasks. (iii) Memory +knowledge management includes knowledge resolution (KR), conflict detection (CD), and answer refusal (AR) subtasks. + + w/o Visual Info. with Visual Info. +memory functionalities. Among them, LoCoMo LoCoMo Mem-Gallery (Ours) + +22.47% +is one of the few that incorporates visual infor- 0.36 -2.11% 0.60 + 0.30 0.54 +15.42% +mation and a multi-session structure. However, it +1.81% + 0.24 0.48 + F1 + + + + + F1 +supports a very restricted multimodal memory func- 0.18 0.42 +tionality evaluation, which is thus always used for -0.63% +5.36% + 0.12 0.36 +text-only memory evaluation (Xu et al., 2025; Fang 0.06 + Full Memory MemGPT A-Mem + 0.30 + Full Memory MemGPT A-Mem +et al., 2025). Furthermore, Figure 2 shows that in- +corporating visual information in LoCoMo yields Figure 2: Effectiveness analysis of visual information +marginal or inconsistent gains, indicating that its with representative memory models. Compared with +evaluation tasks can largely be solved without vi- LoCoMo (Maharana et al., 2024), visual information + plays a much more critical role in Mem-Gallery. +sual clues and thus lack sufficient capacity to assess +multimodal long-term memory. This highlights the + M3-Agent (Long et al., 2025). Although recent +need for a new benchmark that systematically in- + M3-Bench (Long et al., 2025) has taken a step +tegrates task-critical multimodal information into + toward evaluating multimodal long-term memory +multi-session conversations, enabling a comprehen- + with long-video QA, its setup differs fundamen- +sive evaluation of multimodal memory capabilities. + tally from conversational memory with multi-round + interactions. Key challenges to long-term conver- +2.2 Long-Term Agent Memory sational memory, such as multimodal information +Real-world tasks typically require agents to inter- being incrementally introduced, referenced, and +act with their environments in a multi-round and regulated across sessions for personalized assis- +dynamic manner, e.g., multi-round dialogues in tants, remain largely underexplored. +conversational agents, making long-term memory +an important capability for agents (Zhang et al., 3 Mem-Gallery Benchmark +2025b; Wei et al., 2025). Prior works have ex- As shown in Figure 3, we describe Mem-Gallery +plored different aspects of memory design for such from three aspects: (1) the benchmark construction, +agents. For example, Generative agents (Park et al., (2) the unified conversational environment, and (3) +2023) introduced the concept of memory flow for the evaluation framework and task taxonomies. +social event simulation. A-Mem (Xu et al., 2025) +and MemoryOS (Kang et al., 2025) designed agen- 3.1 Benchmark Construction +tic memory construction and maintenance mecha- The statistics of the new dataset can be found in +nisms. However, existing methods primarily focus Table 2. Detailed dataset construction and statistics +on the textual modality, while real-world mem- can be found in Appendix A.2. +ory often requires the joint integration of mul- +timodal information. Consequently, multimodal 3.1.1 Conversation Data +long-term memory has recently garnered growing To support realistic long-term memory evaluation, +interest, such as ViLoMem (Bo et al., 2025) and the conversation data is organized as coherent + Conversational + Benchmark Construction Evaluation Framework + Environment for Memory + Conversation Generation Average 198.1 Rounds Per Conversation + Average 12.0 Sessions Per Conversation + Data Sourcing Data Synthesis Quality Assurance + (1) Base Data Curation + (1) LLM Checking & Editing Multi-Session Conversation + Memory Retrieval & Answer + Generation for Evaluation Tasks + Conv. Generation (with + (2) Background Generation New-Created Story) Multi-Modal + New Story (2) Human Checking & Editing … + Role Profile Creation MLLM-based + Creator Multi-Round + Relevant Topic + Conv. Organization (with + Clustering + Clustered Dialogs) … + Comprehensive Evaluation + Topics + + Evaluation Data Generation … + + Multi-Session + Evaluation Taxonomy Data Synthesis Quality Assurance + Memory Extraction & Adaptation Performance Retrieval Efficiency + Metrics Metrics Metrics + F1, EM, Recall, Organization + BLEU-1, Precision, & Retrieval + Factual Retrieval Visual-centric Search Test-Time Learning + Memory Reasoning Conversations QA Generation LLM + … 10+ Conversation + Memory Agents + LLM-Judge Hit Time + + (LLM) Checking + MLLM Backbone Memory + Temporal Reasoning Visual-centric Reasoning Multi-entity Reasoning Organization and + Memory Knowledge Management Maintenance Benchmark Evaluation + Human Analysis + Evaluation QA Generation Checking + Taxonomy (Human) Stored Memory + Knowledge Resolution Conflict Detection Answer Refusal + + + + + Figure 3: The overall pipeline of our proposed Mem-Gallery. + + Table 2: Statistics of the Mem-Gallery dataset. erate the text-part multi-session dialogues condi- + Mem-Gallery Aspect Statistics tioned on these specifications. Annotators then + Sessions 240 insert appropriate images at suitable positions to + Conversation Data Dialogue Rounds 3,962 ensure multimodal dependency. On the other hand, + Included Images 1,003 + we perform conversation organization via topic- + QA Pairs with Annotated Clues 1,711 + Evaluation Data based clustering, where existing single-session mul- + Included Images 487 + timodal dialogues in MMRC (Xue et al., 2025) are + grouped into candidate multi-session conversations. +multi-session interactions, with consistent user per- Specifically, LLMs are used to extract representa- +sonas and tightly coupled visual & textual content. tive topic keywords from single-session dialogues, + Data Sourcing. We first curate base materi- followed by clustering under constraints on topical +als from open-source resources, including images relevance, fluency, and length. The clustered con- +and parts of the textual content (details in Ap- versations are then reordered and refined to form +pendix A.2.2). Specifically, we prioritize pub- coherent long-term interaction sequences. Details +licly available data that cover diverse everyday and and the example can be found in Appendix A.2.3. +domain-specific scenarios, with sufficient visual de- Quality Assurance. We apply a two-stage qual- +tail and semantic richness to support long-horizon ity assurance process. In the first stage, advanced +multimodal grounding, rather than isolated or short- LLMs automatically check and revise conversa- +context understanding. On top of the curated base tions for coherence, fluency, and factual consis- +data, we further generate structured conversational tency. In the second stage, human annotators care- +backgrounds, such as user role profiles and conver- fully review each conversation and further refine +sation topics. These backgrounds serve as control- the content through manual editing. This ensures +lable anchors for conversation synthesis, ensuring the final conversation data maintains high multi- +topic diversity and long-range coherence. modal quality and realistic conversation dynamics. + Data Synthesis. Based on the sourced mate- +rials, we synthesize multi-session conversations 3.1.2 Evaluation Data +through two complementary strategies, each pro- Based on the curated conversations, we systemat- +ducing valid multi-session conversations that are ically generate evaluation data. It consists of QA +later unified into the final dataset. On one hand, pairs based on a well-predefined evaluation taxon- +we adopt conversation generation with newly cre- omy (details in Section 3.3) for each conversation. +ated stories as conversation backgrounds, where Data Synthesis. Evaluation QA pairs are also +human annotators design the story outline and constructed through two complementary ways, +inter-session transition logic. Advanced LLMs gen- which jointly contribute to the final evaluation set. + On one hand, LLMs are prompted with selected +conversation histories and task descriptions to gen- + Factual Retrieval Multi-entity +erate QA candidates. On the other hand, human an- 12.8% Reasoning + 12.0% +notators construct QA pairs by reviewing each con- +versation and designing targeted questions. These Visual-centric + Memory Reasoning +two ways jointly ensure both coverage, difficulty, Reasoning 10.2% + 29.4% +and diversity of evaluation instances. In addition, Visual-centric Memory + Search Extract. + 17.9% Total: +for each QA pair, we explicitly annotate evidence & Adapt. + 1,711 QA Temporal + 50.4% Reasoning +clues for the correct answer that specify dialogue Pairs + Memory 7.2% + Knowledge +turns in which the relevant information is refer- Management + 20.2% +enced for the answer. These annotated clues facil- Answer + Refusal +itate fine-grained analysis of memory behaviors, 10.8% + Test-Time Learning Conflict +e.g., retrieval details (analyzed in Appendix A.6.2), 19.7% Detection + Knowledge 4.7% +beyond final task performance. Data synthesis de- Resolution + 4.7% +tails can be found in Appendix A.2.4. + Quality Assurance. Generated QA pairs also + Figure 4: Taxonomy and distribution of evaluation tasks. +undergo a two-stage verification. The same as con- +versation generation, LLMs are first used to check retrieval and answer generation are then performed +answer correctness and question clarity. This veri- based on the accumulated memory. The problem +fication step is followed by careful human revision. definition can be found in Appendix A.1. +3.2 Conversational Environment for Memory 3.3 Evaluation Framework +Following previous works (Maharana et al., 2024), As shown in Figure 4, we design three task fam- +memory models are evaluated based on the input ilies to systematically evaluate multimodal long- +conversation. In our setting, memory agents must term memory as an evolving capability in agentic +go beyond storing textual information in existing systems over extended conversations. Specifically, +benchmarks and store and associate visual content memory extraction and adaptation assess whether +with textual content. For textual memory, image agents can retrieve relevant multimodal informa- +captions are provided to preserve the visual content. tion and adapt it as memory accumulates. Memory + Conversation Structure. As shown in Figure 8, reasoning examines whether agents can integrate +each conversation spans multiple sessions with tem- and reason over memory, accounting for temporal +poral gaps. Within each session, agents engage in dependencies, visual evidence, and multiple en- +multi-round multimodal dialogues, while across tities. Memory knowledge management focuses +sessions, information may be updated or contra- on how agents regulate long-term memory under +dicted. This design prevents reliance on short-term dynamic states, like handling inconsistencies, re- +context and explicitly requires memory to persist solving conflicts, and refusing to answer when in- +and evolve across long-term conversational bound- formation is outdated, contradictory, or incomplete. +aries. Visual content in the conversation is not +limited to single-round perception but may be ref- 3.3.1 Memory Extraction & Adaptation +erenced and integrated with textual context across At a fundamental level, memory agents must pos- +sessions. Consequently, agents must integrate mul- sess the capability of memory extraction and adap- +timodal clues distributed over extended timelines, tation, ensuring that stored information can be ef- +rather than treating images as isolated observations. fectively utilized (Hu et al., 2025a). Based on the + Benchmarking Memory Methods. We bench- properties, we design three subtask categories. (1) +mark thirteen representative memory models under Factual Retrieval: evaluate the ability to accurately +a unified setting. As conversations progress, agents recall factual details (e.g., user preferences and past +accumulate an expanding memory. To ensure a fair events) from multimodal interaction histories. (2) +and controlled comparison across diverse designs, Visual-centric Search: evaluate whether the model +all evaluated models follow a unified memory ac- can identify or retrieve specific visual instances +cumulation granularity and protocol (Kang et al., from memory (e.g., shared images). (3) Test-Time +2025), where information is incrementally stored Learning: measure the ability to adapt its memory +along with the conversational timeline. Memory to unseen multimodal examples at inference time. + 3.3.2 Memory Reasoning et al., 2025), as well as representative closed-source +In addition to memory extraction, agents should be MLLMs, namely GPT-4.1-Nano (Achiam et al., +equipped with reasoning capabilities over the mem- 2023) and Gemini-2.5-Flash-Lite (Comanici et al., +ory to address complex tasks (Ke et al., 2025). We 2025), as backbone models. We adopt Qwen2.5- +design three subtasks accordingly. (1) Temporal VL-7B-Instruct as our default MLLM backbone. +Reasoning: assess whether the model can synthe- + Memory Models. We comprehensively include +size and reason over temporally dependent ques- + thirteen representative approaches, including eight +tions from multimodal memory. (2) Visual-centric + textual memory methods and five multimodal mem- +Reasoning: test the model’s capacity to retrieve + ory methods. Specifically, the textual mem- +and utilize visual information as cues for reasoning + ory includes Full Memory (Text), First-in-first- +on multimodal memories. (3) Multi-entity Reason- + out (FIFO), NaiveRAG, Generative Agents (Park +ing: reasoning with multiple entities from memory, + et al., 2023), Reflexion (Shinn et al., 2023), +where each can be textual or visual. + MemGPT (Packer et al., 2023), A-Mem (Xu et al., +3.3.3 Memory Knowledge Management 2025), and MemoryOS (Kang et al., 2025). The +Unlike task-oriented scenarios such as web naviga- multimodal memory includes Full Memory (Mul- +tion, conversations are inherently more open-ended. timodal), MuRAG (Chen et al., 2022), Universal- +Users may update previous information or provide RAG (Yeo et al., 2025), NGM (Fisher, 2025), and +incorrect details inadvertently during dialogues (Hu AUGUSTUS (Jain et al., 2025). Details of these +et al., 2025a). To evaluate these challenges, we de- models are provided in Appendix A.4. For methods +sign the following subtasks. (1) Knowledge Resolu- that require a top-K retriever, we adopt a default +tion: Examine the ability to correctly update stored retrieval size of K=10. To enable a fair comparison +knowledge when new, contradictory information with multimodal memory systems, we provide tex- +appears in the dialogue, maintaining consistency tual memory with high-quality image captions gen- +over time. (2) Conflict Detection: Test whether the erated by GPT-5.1. The detailed benchmark setup +model can detect conflicts between newly observed for fair comparison is shown in Appendix A.5. +information and existing memory. (3) Answer Re- +fusal: Assess the model’s capability to abstain or 4.1.2 Evaluation Metrics +refuse to answer when the requested information is Following existing works, we evaluate memory +unsupported by prior memory. performance using F1, BLEU-1, EM, and LLM-as- + a-Judge metrics. For LLM-as-a-Judge, we adopt +4 Benchmarking Analysis Qwen-2.5-72B-Instruct (Bai et al., 2025) as the +We conduct extensive experiments on our proposed judging model. Note that the conflict detection task +Mem-Gallery for the following research questions. explicitly requires models to output either “Yes” or +• RQ1: How effective are different multimodal “No”. Since MLLMs possess instruction-following + storage designs? capability, the values of all metrics are identical for + this task. For retrieval analysis, we adopt the widely +• RQ2: How important are memory organization used Recall, Precision, and Hit as the metrics. The + strategies in multimodal memory systems? details can be found in Appendix A.5.4. +• RQ3: What are the strengths and weaknesses of + different memory models across task types? 4.2 Main Results (RQ1-RQ3) +• RQ4: What are the runtime efficiency character- Table 3 illustrates the main benchmarking results. + istics of different memory approaches? We can have the following observations. +• RQ5: How does the number of retrieved memory RQ1: Explicit multimodal memory preserva- + entries affect the overall performance? tion is critical, but increased architectural com- + plexity does not necessarily bring better perfor- +4.1 Evaluation Setup + mance. Although with high-quality image cap- +4.1.1 Model Implementation tions, textual memory baselines still generally ex- +MLLM Backbones. To ensure a broad coverage hibit a performance gap compared to multimodal +of model capacities, we use representative open- approaches, particularly in memory extraction and +source MLLMs at two model scales, Qwen2.5- adaptation. MuRAG, a simple multimodal method, +VL-3B-Instruct and Qwen2.5-VL-7B-Instruct (Bai can achieve the best overall performance among + Table 3: Main evaluation results on our Mem-Gallery based on Qwen-2.5-VL-7B. The best and second-performed +memory model(s) are highlighted with orange and blue backgrounds. Results on Qwen-2.5-VL-3B, GPT-4.1-Nano, +and Gemini-2.5-Flash-Lite can be found in Table 8, Table 9, and Table 10, respectively. + Qwen-2.5-VL-7B Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2376 0.1446 0.5852 0.2424 0.2391 0.5928 0.6072 0.6244 0.2150 0.6724 0.6632 0.6364 0.6162 + BLEU-1 0.1865 0.1000 0.5045 0.1862 0.1903 0.5098 0.5138 0.5354 0.1626 0.5755 0.5658 0.5606 0.5309 + FR EM 0.0913 0.0457 0.3059 0.0868 0.0913 0.3288 0.2922 0.3470 0.0685 0.3607 0.3470 0.3744 0.3288 + LLM-Judge 0.2626 0.1324 0.7763 0.2945 0.2626 0.8539 0.7808 0.8265 0.2260 0.8790 0.8744 0.8082 0.8082 +Extract. & Adapt. + + + + + F1 0.1992 0.0612 0.7592 0.2970 0.1954 0.6239 0.7681 0.7853 0.1658 0.8818 0.8708 0.8531 0.8499 + BLEU-1 0.1873 0.0549 0.7063 0.2616 0.1840 0.5834 0.6880 0.7181 0.1473 0.8442 0.8343 0.8145 0.7942 + VS EM 0.1601 0.0392 0.5686 0.2124 0.1569 0.4118 0.5882 0.6046 0.1078 0.6699 0.6699 0.6863 0.6536 + LLM-Judge 0.1961 0.0556 0.7402 0.2958 0.1895 0.5964 0.7369 0.7729 0.1683 0.8856 0.8611 0.8480 0.8399 + F1 0.4500 0.3351 0.6526 0.4851 0.4486 0.2924 0.6336 0.5484 0.4147 0.8177 0.7824 0.7817 0.7913 + BLEU-1 0.3799 0.2692 0.5835 0.4150 0.3798 0.2295 0.5595 0.4697 0.3477 0.7449 0.7103 0.7168 0.7206 + TTL EM 0.2374 0.1365 0.4718 0.2967 0.2374 0.1009 0.4362 0.3561 0.2107 0.6172 0.6142 0.6024 0.6113 + LLM-Judge 0.7092 0.6677 0.8457 0.7582 0.7033 0.7092 0.7997 0.7715 0.7107 0.9006 0.8501 0.9110 0.8932 + F1 0.2545 0.1549 0.4887 0.2742 0.2553 0.5661 0.5604 0.5497 0.2294 0.5833 0.5460 0.5425 0.5800 + BLEU-1 0.2363 0.1316 0.4587 0.2473 0.2363 0.5326 0.5361 0.5240 0.2065 0.5537 0.5137 0.5143 0.5527 + TR EM 0.1545 0.0894 0.3496 0.1626 0.1545 0.3496 0.4065 0.3821 0.1382 0.4309 0.4065 0.4146 0.4390 + LLM-Judge 0.2805 0.1463 0.6382 0.3252 0.2764 0.8008 0.6951 0.7195 0.2480 0.7724 0.7520 0.7195 0.7398 + F1 0.2552 0.1207 0.3022 0.1955 0.2594 0.4593 0.4477 0.4280 0.2015 0.4818 0.4879 0.4615 0.3866 +Reasoning + + + + + BLEU-1 0.2442 0.1005 0.2873 0.1815 0.2480 0.4459 0.4331 0.4126 0.1912 0.4625 0.4682 0.4428 0.3726 + VR EM 0.2011 0.0690 0.1954 0.1207 0.2011 0.3851 0.3563 0.3391 0.1609 0.3793 0.3793 0.3678 0.2931 + LLM-Judge 0.3046 0.1408 0.3793 0.2471 0.3046 0.6149 0.5747 0.5805 0.2586 0.6092 0.5977 0.5460 0.4856 + F1 0.2411 0.1745 0.4640 0.2450 0.2428 0.4367 0.5000 0.4490 0.2101 0.5007 0.5013 0.4746 0.4866 + BLEU-1 0.1739 0.1184 0.3543 0.1784 0.1770 0.3347 0.3908 0.3446 0.1429 0.3903 0.3868 0.3635 0.3778 + MR EM 0.0340 0.0146 0.0874 0.0243 0.0340 0.0631 0.0728 0.0728 0.0194 0.0874 0.0971 0.0728 0.0825 + LLM-Judge 0.2985 0.1602 0.7791 0.3350 0.3058 0.8204 0.8083 0.8204 0.2791 0.8447 0.8422 0.7840 0.8228 + F1 0.2354 0.1697 0.3595 0.2674 0.2354 0.4292 0.4515 0.5031 0.2181 0.4601 0.4336 0.3944 0.3752 + BLEU-1 0.2005 0.1424 0.2986 0.2341 0.2000 0.3741 0.3992 0.4479 0.1883 0.4073 0.3772 0.3444 0.3159 + KR EM 0.1235 0.0864 0.1605 0.1235 0.1235 0.2099 0.2469 0.2716 0.1235 0.2593 0.2099 0.2099 0.1728 +Knowledge Management + + + + + LLM-Judge 0.3395 0.2469 0.6358 0.3457 0.3395 0.7407 0.6667 0.7593 0.2840 0.7840 0.7222 0.6728 0.6728 + F1 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 + BLEU-1 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 + CD EM 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 + LLM-Judge 0.3457 0.3580 0.3457 0.3210 0.3333 0.3580 0.3333 0.3333 0.3580 0.3704 0.3457 0.3457 0.3210 + F1 0.9958 1.0000 0.9581 0.9841 0.9958 0.9849 0.9278 0.9845 0.9946 0.9418 0.9473 0.9580 0.9460 + BLEU-1 0.9953 1.0000 0.9575 0.9839 0.9953 0.9844 0.9257 0.9841 0.9946 0.9409 0.9466 0.9574 0.9459 + AR EM 0.9946 1.0000 0.9565 0.9837 0.9946 0.9837 0.9239 0.9837 0.9946 0.9402 0.9457 0.9565 0.9457 + LLM-Judge 0.9783 0.9837 0.9402 0.9674 0.9783 0.9674 0.9375 0.9674 0.9837 0.9375 0.9429 0.9457 0.9429 + F1 0.3625 0.2724 0.5974 0.3825 0.3619 0.5282 0.6228 0.6109 0.3354 0.6966 0.6827 0.6691 0.6610 + BLEU-1 0.3279 0.2408 0.5441 0.3422 0.3279 0.4792 0.5629 0.5534 0.2999 0.6432 0.6286 0.6200 0.6069 + Overall EM 0.2519 0.1835 0.4161 0.2613 0.2507 0.3402 0.4296 0.4278 0.2279 0.4985 0.4927 0.4944 0.4757 + LLM-Judge 0.4331 0.3369 0.7241 0.4643 0.4307 0.7306 0.7431 0.7613 0.4129 0.8229 0.8016 0.7861 0.7797 + + + +well-designed textual memory models. Specifically, and MuRAG. As visual information is far more +it achieves 11.85%, 7.69%, 12.29%, and 29.06% F1 token-heavy than text (Zhang et al., 2025a), under +improvement over the best-performed textual mem- the token limit, naïvely accumulating multimodal +ory on overall, FR, VS, and TTL tasks, respectively. content can introduce irrelevant visual noise, crowd +Furthermore, MuRAG and UniversalRAG, which out informative text, and thus hurt performance. +simply preserve multimodal information without Besides, compared to MuRAG and UniversalRAG, +structured memory organizations, achieve stronger although existing models like NGM and AUGUS- +performance than complex multimodal memory TUS organize multimodal information in a struc- +systems. For example, MuRAG outperforms 4.11% tured manner, they still lack effective strategies for +and 5.39% overall F1 improvements over NGM and long-term conversational scenarios. Moreover, ex- +AUGUSTUS, respectively. This contrast suggests isting multimodal methods still lack agentic main- +that, beyond focusing on the memory architecture tenance strategies like A-Mem and MemoryOS, +itself, how to effectively preserve multimodal infor- which also constrain their performance. +mation so as to maximize the utility of the memory RQ3-1: Existing multimodal memory meth- +architecture remains an open challenge. ods struggle on memory reasoning tasks. Al- + RQ2: Visual information faces harsher to- though multimodal memory methods overall out- +ken consumption, making principled organi- perform textual memory approaches, we observe +zation and maintenance critical. Without the that their advantages are not pronounced in scenar- +proper organization, inputting all multimodal infor- ios requiring reasoning. This is particularly evident +mation into the context leads Full Memory (MM) in multi-entity reasoning and temporal reasoning +to perform even worse than Full Memory (Text) tasks. Notably, even in the visual-centric reasoning +in both memory extraction and reasoning tasks. task that explicitly relies on visual clues, MemGPT, +Overall, Full Memory (MM) performs 8.08% and a textual memory method, can achieve near-optimal +51.85% worse F1 score than Full Memory (Text) performance. This suggests that existing multi- + Textual Memory + Multimodal Memory + Table 4: Retrieval metrics on different size K. + 103 +Memory time (s) + + + Recall@K K=5 K=10 K=15 K=20 + 101 + Gen. Agent 0.1707 0.2385 0.2854 0.3153 + 10 1 NaiveRAG 0.5381 0.6723 0.7420 0.7877 + MuRAG 0.7506 0.8601 0.8990 0.9228 + t) FIFO G nt n T OS M) AG AG S + (Tex eRA n. Age eflexio emGP em + A-M emory Full (M MuR ersalR NGM USTU UniversalRAG 0.7311 0.8411 0.8781 0.8998 + Full Naiv Ge R M AUG + M Univ NGM 0.6192 0.7475 0.7892 0.8065 + Retrieval & generation time (s) + + + + + 104 AUGUSTUS 0.6729 0.7529 0.7785 0.7860 + Precision@K K=5 K=10 K=15 K=20 + Gen. Agent 0.0955 0.0684 0.0544 0.0453 + 103 NaiveRAG 0.2694 0.1757 0.1315 0.1047 + MuRAG 0.3686 0.2220 0.1572 0.1220 + t) FIFO G nt n T OS M) AG AG S + (Tex eRA n. Age eflexio emGP em + A-M emory Full (M MuR ersalR NGM USTU UniversalRAG 0.3691 0.2206 0.1555 0.1204 + Full Naiv Ge R M AUG + M Univ NGM 0.3564 0.3457 0.3450 0.3424 + AUGUSTUS 0.3341 0.2488 0.2213 0.2124 + Figure 5: Efficiency comparison results on information + memorization time (top) and memory retrieval & answer and MemoryOS. This suggests that introducing + generation time (bottom) in seconds with the log scale. multimodal signals fundamentally increases system + complexity, regardless of whether advanced mem- +modal memory approaches still primarily focus on ory organization strategies are employed. Thus, +the storage and retrieval of multimodal informa- while multimodal memory can provide richer con- +tion, yet how to effectively reason over multimodal textual information, it also introduces considerable +contents in memory remains largely unexplored. inference-time overhead, which may limit its practi- + RQ3-2: Existing methods show limitations in cality in long-horizon or real-time agent scenarios. +scenarios involving information updates or con- +flicts. From the memory knowledge management 4.4 Retriever Analysis (RQ5) +results, we derive two key observations. First, there Table 4 shows multimodal retrievers like MuRAG +is a trade-off in refusal behavior. Methods with and UniversalRAG achieve substantially higher +weaker memory, e.g., FIFO, tend to exhibit stronger clue recall. Though recall improves as K increases, +refusal performance, as they default to refusing the expanded retrieval coverage does not con- +when relevant information cannot be retrieved. In sistently translate into better QA performance. +contrast, methods with stronger capabilities on Figure 15 shows multimodal memory models typ- +memory extraction and reasoning show poorer re- ically exhibit diminishing or saturated task gains +fusal performance, like A-Mem and MuRAG, with beyond a moderate K, and may degrade at larger +7.22% and 5.82% worse F1 score than FIFO. This K. Table 4 explains this discrepancy. While the +suggests that future methods need to strike a bal- Recall of MuRAG and UniversalRAG increases +ance between retrieving relevant information and as K grows, its Precision drops sharply, indicat- +distinguishing outdated and conflicting informa- ing the introduction of substantial noise. NGM +tion to build safe and hallucination-free memory and AUGUSTUS exhibit more conservative recall +systems. Second, for both knowledge resolution growth with relatively stable Precision, suggesting +and conflict detection tasks, neither multimodal nor structured organization or relevance filtering is an +textual memory methods achieve satisfactory per- effective way to balance coverage and noise. Full +formance. This indicates that designing memory analysis and results are reported in Appendix A.6.2. +systems to support dynamic conflict detection and +memory correction remains an important direction. 5 Conclusion + Full analysis under different MLLMs and case In this paper, we present Mem-Gallery, a bench- +studies can be found in Appendix A.6.1 and A.7. mark for evaluating multimodal long-term conver- + sational memory. By grounding multi-session con- + 4.3 Efficiency Analysis (RQ4) versations in tightly coupled visual and textual con- + Figure 5 presents an efficiency comparison of exist- texts, Mem-Gallery enables systematic assessment + ing memory methods. From the results, we can see beyond prior text-centric memory or localized mul- + that multimodal memory incurs higher overhead timodal context understanding benchmarks. Exten- + than text-only memory in general. Even a rela- sive evaluation reveals several takeaways. These + tively simple multimodal memory like MuRAG can findings highlight the need for principled memory + approach the computational cost of highly sophis- organization, selective retrieval, and robust mainte- + ticated textual memory systems, such as A-Mem nance for future multimodal memory design. + Limitations next generation agentic capabilities. arXiv preprint + arXiv:2507.06261. +While Mem-Gallery provides a comprehensive +evaluation of multimodal long-term conversational Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, + Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi +memory, the following limitations remain. First, Yao, Mengru Wang, Shuofei Qiao, and 1 oth- +the benchmark focuses on vision–language con- ers. 2025. Lightmem: Lightweight and efficient +versational settings and does not explicitly cover memory-augmented generation. arXiv preprint +other modalities such as audio or embodied signals, arXiv:2510.18866. +which may be relevant in broader agentic scenar- Matthew Fisher. 2025. Neural graph memory: A struc- +ios. Second, the evaluation primarily focuses on tured approach to long-term memory in multimodal +memory-centric capabilities in long-horizon con- agents. +versations and does not aim to exhaustively assess + Yuanzhe Hu, Yu Wang, and Julian McAuley. 2025a. +other agent behaviors, such as planning or tool use. 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In Pro- + ceedings of the AAAI Conference on Artificial Intelli- + gence, volume 38, pages 19724–19731. + A Appendix +Contents + A.1 Problem Definition . . . . . . . . 13 + A.2 Data Construction Details and + Statistics . . . . . . . . . . . . . . 13 + A.2.1 Data Statistics . . . . . . 13 + A.2.2 Data Source . . . . . . . . 13 + A.2.3 Conversation Data Synthesis 14 + A.2.4 Evaluation Data Synthesis 16 + A.3 Detailed Evaluation Task Descrip- + tions . . . . . . . . . . . . . . . . 16 + A.4 Baseline Details . . . . . . . . . . 18 + A.4.1 Textual Memory with Vi- + sual Caption . . . . . . . 18 + A.4.2 Multimodal Memory . . . 18 + A.5 Benchmark Evaluation Details . . 19 + A.5.1 Experimental Environments 19 + A.5.2 Benchmark Settings . . . 19 + A.5.3 Evaluation Pipeline . . . . 19 + A.5.4 Evaluation Metrics . . . . 20 + A.6 Additional Results and Analysis . 21 + A.6.1 MLLM Backbone Analysis 21 + A.6.2 Memory Retrieval Analysis 24 + A.7 Case Study . . . . . . . . . . . . 26 + A.8 Ethics Statement . . . . . . . . . 28 + A.8.1 Data Privacy . . . . . . . 28 + A.8.2 Annotation . . . . . . . . 28 + A.8.3 Scientific Artifacts . . . . 29 + A.9 Potential Risks . . . . . . . . . . 29 + A.10 Use of AI Assistants . . . . . . . 30 + A.1 Problem Definition Memory-Augmented Generation. The final ac- +Notations. We consider an MLLM-based agent tion or response yτ is generated by the MLLM +interacting with an environment (user) over a se- agent by conditioning on the fusion of the working +quence of time steps t ∈ {1, . . . , T }. Let O = context Cτ , retrieved external knowledge Mret , +{o1 , o2 , . . . , oT } be a stream of multimodal obser- and the specific task query qτ at timestamp τ : +vations, where each observation entry ot = ⟨vt , st ⟩ +may contain visual vt and textual st modalities. yτ = MLLM(Cτ ⊕ Mret ⊕ qτ ), (3) +The agentic memory system is defined by the tuple +S = ⟨M, fθ , Φ, R⟩, where: where ⊕ denotes the integration of heterogeneous + tokens. This architecture ensures that the agent can + • Mt = {m1 , m2 , . . . , mn } is the external mul- maintain long-term coherence and persona consis- + timodal memory, an unbounded set of atomic tency without being limited by the context window + units. Each unit mi encapsulates raw assets, of the underlying model (Zhang et al., 2025b). + cross-modal descriptions, and a joint latent + embedding ei . A.2 Data Construction Details and Statistics + A.2.1 Data Statistics + • fθ is a multimodal encoder that projects het- + erogeneous data into a unified d-dimensional Mem-Gallery is a new benchmark featuring: (1) + latent space Rd . scenario diversity, with long, multimodal, and + multi-session conversations spanning twenty sce- + • Φ is the update operator that governs the tran- narios; (2) non-trivial multimodality, where visual + sition of information between new informa- and textual information are genuinely complemen- + tion in O and M, including memory consoli- tary and both important; and (3) multi-faceted ca- + dation and eviction. pabilities, comprehensively assessing key mem- + ory abilities including extraction, test-time adapta- + • R is the retrieval operator used to identify and tion, reasoning, and knowledge management. The + surface relevant historical information from dataset in Mem-Gallery is constructed across 20 + M based on current needs. diverse conversation scenarios, comprising a total + of 240 multi-session dialogues with 3,962 conver- +Memory Construction and Maintenance. The sational rounds. It incorporates 1,003 input images, +memory evolution occurs continuously as the agent which are naturally grounded within the dialogue +ingests the observation stream O from the conver- context. The details of each scenario can be found +sation. At each time step t, the update operator in Table 5. Based on these dialogues, we further cu- +Φ consolidates new observations into the existing rate 1,711 human-annotated question–answer pairs +memory repository: to support systematic evaluation. Among them, + 487 questions are explicitly associated with visual + Mt+1 = Φ(Mt , ot , πevo ), (1) inputs, requiring models to reason over both tex- + tual and visual information. The details can be +where πevo represents an autonomous evolution + found in Table 6. This design ensures broad cov- +policy, such as memory add, merge, or delete (Yan + erage of multimodal conversational settings while +et al., 2025; Xiong et al., 2025). + enabling fine-grained analysis of long-term multi- +Memory Retrieval. Generation is invoked at a modal memory capabilities. +specific task timestamp τ . Given a multimodal + A.2.2 Data Source +user query qτ , the retrieval operator R surfaces a +relevant subset Mret from the current state of the Image Data Source. To ensure sufficient eval- +memory repository: uation difficulty and diversity, we collect fine- + grained image categories as well as images that +Mret = {mi ∈ Mτ | rank(sim(fθ (qτ ), fθ (mi ))) ≤ K}, are closely related to the manually designed di- + (2) alogue topics as multimodal conversation con- +where sim(·) is a similarity scoring function which struction materials. All collected images are se- +computes the multimodal similarity and K is the lected from sources with permissive knowledge- +retrieval size. sharing licenses. These image resources include + Table 5: Detailed statistics of the new dataset in our Mem-Gallery. Each multi-session conversation scenario +contains multiple related dialogue topics. + + Scenarios Sessions Rounds Images in Dialogs QAs Images in Questions + AI, Robotics, Automation, Future Tech 12 185 31 57 8 + Academic, Animal, Pet, Research, Life 14 177 45 77 28 + Architecture, Art, Culture, Exhibition, Technology 14 186 30 74 6 + Astronomy, Physics, Scientific Experiments, Cosmology 11 203 30 54 2 + Baking, Dessert, Daily Life, Skill 15 262 57 111 32 + Dog, Behavior, Research, Academic, Life 11 180 38 63 21 + Education, Career, Research, Lifestyle 12 227 56 106 21 + Entrepreneurship, Blockchain, Economics, Logistics, Nature 10 144 36 90 25 + Fashion, Personal Care, Lifestyle, Shopping 9 168 37 61 13 + Global Travel, Culture, Sightseeing 10 176 66 87 29 + Global Travel, Sustainable Fashion, Design 9 159 53 78 29 + Home, Health, Lifestyle, Product 10 163 36 74 8 + Home, Repair, Maintenance, Cleaning 18 270 43 68 0 + Landscape, Travel, Architecture, Nature 11 173 64 92 38 + Music, Dance, Theater, Performance, Learning 14 220 43 57 1 + Nature, Economics, Programming, Student Life 10 201 78 136 67 + Parenting, Commuting, Hobbies, Travel, Gear 16 221 48 82 14 + Python, Botany, AI, Student Life 12 252 117 205 110 + Real Estate, Home Decor, DIY, Lifestyle 11 196 53 75 11 + Technology, Ethics, Future Society 11 199 42 64 24 + Total Statistics 240 3,962 1,003 1,711 487 + + + +MMDU2 (Liu et al., 2024), CUB-200-20113 (Wah details can be found in Appendix A.2.3. +et al., 2011), Stanford Dogs4 (Khosla et al., 2011), + A.2.3 Conversation Data Synthesis +Oxford flower5 (Nilsback and Zisserman, 2008), +and DeepFashion6 (Liu et al., 2016). After collec- As shown in Figure 6, our conversation data syn- +tion, we manually select appropriate images and thesis follows two main approaches. +insert them at information-relevant positions within First, we generate high-quality conversational +the generated textual conversations. The details can stories with the assistance of human annotators. +be found in Appendix A.2.3. Annotators initially create the protagonist’s profile + Single-Session Dialogue Data Source. In and overall conversation themes, and then spec- +addition to newly created materials, we recon- ify the core content of each session through story +struct existing single-session dialogues with mul- outlines, as well as the transition logic between +timodal information, sourced from prior dataset sessions. Based on this setup, dialogue generation +MMRC7 (Xue et al., 2025). Specifically, we first proceeds in two stages. LLMs are first used to +employ advanced LLMs, namely GPT-5.1 and generate the textual dialogue content. Specifically, +Gemini-2.5-Pro, to extract representative keywords we employ GPT-5.1 and Gemini-2.5-Pro in parallel +from each single-session dialogue, after which and manually select higher-quality outputs. The +the higher-quality outputs are manually selected. dialogues are then further refined, and appropriate +Based on the extracted keywords, we further use images are inserted at suitable positions to establish +LLMs to perform session-level clustering. Once multimodal dependencies. Each multimodal multi- +candidate multi-session dialogues are formed, we session long conversation is constructed following +conduct additional human filtering and enhance- this process, after which quality assurance and QA +ment to ensure the overall quality and coherence of generation are conducted. +the reconstructed multi-session conversations. The Second, we reconstruct existing single-session + dialogue datasets via topic-based clustering. In this + 2 + https://github.com/Liuziyu77/MMDU approach, LLMs are first used to extract session- + 3 + https://www.vision.caltech.edu/datasets/cub_ level topic keywords and perform clustering. Hu- +200_2011 + 4 man annotators then review and refine the clustered + http://vision.stanford.edu/aditya86/ +ImageNetDogs results, and summarize the corresponding user pro- + 5 + https://www.robots.ox.ac.uk/~vgg/data/ files and multi-session conversation topics. Since +flowers/102 the resulting clustered long conversations often suf- + 6 + https://mmlab.ie.cuhk.edu.hk/projects/ +DeepFashion.html fer from limited fluency, weak inter-session transi- + 7 + https://github.com/haochen-MBZUAI/MMRC tion logic, and insufficient length, we further refine + Story Outline & Inter-Session Transition Logic for + User Profile: Each Conversation + { Thinking of Adopting and AI for animal Online animal Classmate’s dog & Road Travel + "name": "Lena", adopting a dog naming a dog behavior behavior seminar discuss breeds with the dog + “persona_summary”: "Lena is a 21-year-old + university student majoring in Life Sciences. … … + Sessions + She has always been fascinated by animals and + spends much of her free time volunteering at S-1 S-2 S-3 S-4 S-k S-n + the campus animal care center. ...", + "traits": ["curious", "caring", "enthusiastic"], + “conversation_style”: "Warm and inquisitive, + Lena’s conversations often blend empathy with Conversation Generation with LLMs and + scientific curiosity. She expresses genuine Image Insert by Human Annotators + interest in understanding how things work ..." + } + Background Session-1 Session-n + Creation + … … + … + Academic, Animal, User … User … + Pet, Research, Life Assistant Assistant + … … + + Conversation Topics + Quality Assurance & + QA Generation + (a) Conversation Generation with Newly Created Story + + + User Profile: + … { + "name": "Julian", Reorder, Refinement, and Include more Sessions + “persona_summary”: "Julian is a 19-year- by Human Annotators + old first-year undergraduate student in + Single-Session Dialog architecture, fascinated by how historical + Clustering spaces and modern technology can shape Session-1 Session-n + human emotion and memory.", + “traits”: [“inquisitive”, “introspective”, + … … + "creative", "methodical"], … + “conversation_style”: "Thoughtful and + User … User … + inquisitive, often exploring concepts ..." Assistant Assistant + … … + } + + + + Architecture, Art, + Refinement and + Culture, Exhibition, + Summarization Technology Quality Assurance & + QA Generation + + Conversation Topics + + (b) Conversation Generation with Topic-based Clustering + + + Figure 6: Illustration of the generation for each conversation, including two ways. + + +the dialogue content to ensure coherence and rea- /R&R0R + 0HP*DOOHU\ +  +sonable session transitions. To address insufficient +  +conversation length, we additionally extend these + 3HUFHQWDJHRI6HVVLRQV  + + + + +clustered conversations by introducing more ses-  + + +sions, which are generated by LLMs and then man-  + +ually verified and augmented with images, while  + +preserving logical continuity across sessions. +  + + The distribution of image numbers per ses-  + +sion in Mem-Gallery is described in Figure 7. +  +                  +From the distribution, we observe that, beyond 1XPEHURI,PDJHVSHU6HVVLRQ +the importance and high quality of visual infor- +mation in Mem-Gallery as compared in Figure 2, Figure 7: Image number distribution of Mem-Gallery +Mem-Gallery significantly reduces the proportion per conversation session compared with LoCoMo. +of sessions with no images or only a single image +compared to LoCoMo (Maharana et al., 2024). The +overall distribution shifts rightward, with most ses- 17 images. This design increases both the density +sions containing at least two images, and the num- of visual information in conversations and the over- +ber of sessions with multiple images (e.g., seven all difficulty of the dialogue tasks. The conversa- +or more images) is substantially increased. In the tion structure of Mem-Gallery and the information +most extreme cases, a single session contains up to feed way for memory agents are shown in Figure 8. + Table 6: Evaluation task statistics on the QA number quire explicit visual cues are entirely constructed +and average clues in each category. * The AR task is by human annotators. This process emphasizes +designed to evaluate refusal capability. The information + challenging cases that are difficult for automatic +in the question does not exist in the dialogue, and there- +fore clues are unsupported. generation to reliably capture. + For each finalized QA pair, annotators explicitly + Evaluation Tasks Number of QAs Avg. Clues annotate the evidence clues required to derive the + FR 219 2.90 correct answer with the assistance of LLMs. These + Extract. & Adapt. VS 306 1.91 clues specify the dialogue turns or visual content + TTL 337 4.09 + where the relevant information is introduced or + TR 123 1.74 + VR 174 3.05 + referenced. + Reasoning + MR 206 2.17 + KR 81 2.21 + A.3 Detailed Evaluation Task Descriptions + Knowledge Management CD 81 1.70 Mem-Gallery evaluates multimodal long-term con- + AR* 184 — + versational memory through three complementary + Overall 1,711 2.69 + functional dimensions, each consisting of three + fine-grained task types. Together, these nine tasks + provide a systematic and comprehensive assess- +A.2.4 Evaluation Data Synthesis + ment of how memory is extracted, adapted, rea- +Evaluation QA pairs are constructed through soned over, and managed under long-horizon mul- +two complementary and independent generation timodal conversational settings: +pipelines, which jointly contribute to the final eval- +uation set. These two pipelines are designed to Memory Extraction & Adaptation. This dimen- +balance coverage, difficulty, and diversity while sion focuses on whether an agent can correctly +ensuring that evaluation instances genuinely cover extract, store, and adaptively utilize information +long-term and multimodal memory access. accumulated throughout long-term multimodal in- + In the first pipeline, advanced LLMs are teractions. +prompted to automatically generate candidate QA + • Factual Retrieval (FR): This task evaluates +pairs. The input to the LLM consists of selected + the model’s ability to accurately recall ex- +multi-session conversation histories together with + plicit factual information mentioned in pre- +detailed task descriptions specifying the targeted + vious multimodal dialogue turns, including +evaluation capability (e.g., factual retrieval, tem- + user attributes, preferences, entities, events, +poral reasoning, or knowledge resolution). The + and decisions. Questions may require retriev- +prompts explicitly constrain questions to depend + ing information introduced long before the +on information distributed across multiple dialogue + query, testing whether the memory system can +turns or sessions, and, when applicable, to require + preserve and access long-horizon facts rather +visual evidence in addition to textual context. This + than relying on short-term context. +pipeline enables scalable generation of QA candi- +dates with broad task coverage. • Visual-centric Search (VS): This task as- + In parallel, human annotators manually construct sesses whether the model can correctly and +QA pairs by reviewing each entire multi-session explicitly identify or retrieve relevant visual +conversation from start to end. Annotators are in- content from the conversation history. Queries +structed to identify information dependencies that typically require selecting the correct image +span long temporal gaps, involve updates or contra- or images associated with a specific entity, +dictions, or require integrating visual and textual event, or user reference, testing the alignment +cues. Based on these observations, annotators de- between visual memory entries and textual +sign targeted questions that cannot be answered cues in long-term memory. +from local context alone and instead require ac- +cessing accumulated multimodal memory. Notably, • Test-Time Learning (TTL): This task mea- +LLMs are unable to reliably generate QA pairs that sures the model’s ability to adapt its under- +explicitly depend on visual evidence. The result- standing at inference time based on newly +ing questions are often inaccurate or of low quality. provided multimodal examples. The agent +Therefore, evaluation instances for tasks that re- must incorporate new multimodal instances + Session-1 Session-k Session-n + 2024-05-12 2024-08-10 2024-11-29 + + + … … … + + User … User … User … + Assistant Assistant Assistant + + … Multi-Round … … … … + + … … … + + User … User … User … + Assistant Assistant Assistant + + + Multi-Session + Streaming Interaction + Memory Action: + … {Add; Delete; Update} + + User … + Assistant + + Memory Agent Stored Memory + + + Figure 8: Structure example of each conversation. + + + into its existing memory and apply this up- sesses the model’s capacity to jointly reason + dated knowledge to answer subsequent ques- over multiple entities stored in memory, where + tions, without any parameter updates. This entities may be introduced at different times + setting emphasizes online memory adaptation and may involve both textual and visual rep- + rather than static memorization. resentations. Correct answers require synthe- + sizing information across entities rather than +Memory Reasoning. Beyond direct retrieval, recalling isolated facts. +real-world conversational agents must reason over +stored memories. This dimension evaluates Memory Knowledge Management. Conversa- +whether models can perform structured reasoning tional memory is inherently dynamic and imperfect. +using long-term multimodal information accumu- This dimension evaluates whether a memory sys- +lated over time. tem can properly manage evolving, conflicting, or + missing information. + • Temporal Reasoning (TR): This task evalu- + ates whether the model can reason over tem- • Knowledge Resolution (KR): This task ex- + porally ordered information in memory. Ques- amines whether the model can correctly up- + tions may involve comparing events across date its memory when new information contra- + different sessions, identifying the order of dicts previously stored knowledge. The agent + occurrences, or determining when a partic- must discard or revise outdated beliefs and + ular event happened based on dispersed multi- consistently rely on the most recent and cor- + modal cues. rect information when answering questions. + + • Visual-centric Reasoning (VR): This task • Conflict Detection (CD): This task evaluates + tests the model’s ability to use visual infor- the model’s ability to detect inconsistencies + mation as a key component of the reasoning between newly observed information and ex- + process. Unlike visual-centric search, which isting memory. Rather than resolving the con- + focuses on retrieval, visual-centric reasoning flict, the agent is required to explicitly identify + requires integrating visual cues with textual whether a contradiction exists, testing sensi- + context to infer properties, relationships, or tivity to memory consistency. + similarities that are not explicitly stated. + • Answer Refusal (AR): This task assesses + • Multi-entity Reasoning (MR): This task as- whether the model can appropriately refuse to + answer when the requested information has model dynamically pages relevant memories + never been mentioned or is unsupported by into context and evicts less useful ones, en- + the conversation history. It tests whether the abling effective long-term conversation be- + memory system avoids hallucination and rec- yond fixed context limits. + ognizes the absence of valid evidence in long- + term memory. • A-Mem (Xu et al., 2025): A-MEM pro- + poses an agentic memory system that orga- +A.4 Baseline Details nizes experiences as structured atomic notes + with rich contextual descriptions, inspired +We include thirteen memory methods, with eight + by the Zettelkasten method. New memories +representative textual memory methods and five + autonomously establish links to related past +multimodal memory methods. + memories and can trigger updates to existing +A.4.1 Textual Memory with Visual Caption ones, enabling a dynamically evolving mem- +For the textual memory, we transfer the raw image ory network that supports long-term reasoning +into an image caption with GPT-5.1. The details of without predefined memory operations. +these models are illustrated as follows. + • MemoryOS (Kang et al., 2025): MemoryOS + • Full Memory (Text): It includes all memory introduces an operating system–inspired mem- + information in textual form as part of the con- ory framework that manages agent memory + text, and truncates it according to the context through hierarchical storage, dynamic updat- + token limit. ing, and semantic retrieval. It organizes mem- + ory into short-term, mid-term, and long-term + • FIFO (First-in-first-out): It selects the most persona layers, and applies OS-style mecha- + recent memory information as context accord- nisms such as FIFO, segmented paging, and + ing to the temporal order of the memory. heat-based eviction to maintain coherent and + personalized long-term conversations. + • NaiveRAG: It encodes the memory informa- + tion into semantic vectors, then retrieves and A.4.2 Multimodal Memory + returns the most similar memory entries based For the multimodal memory, we include five repre- + on vector similarity. sentative models. The details of these models are + illustrated as follows. + • Generative Agent (Park et al., 2023): It intro- + duces a generative agent memory that stores • Full Memory (Multimodal): It includes all + experiences as language memories and re- multimodal memory information as context, + trieves relevant entries to condition agent be- estimates the token consumption of images + havior. It further synthesizes higher-level re- using predefined token costs, and truncates + flections from past experiences to support co- the input according to the context token limit. + herent long-term planning and interaction. + • MuRAG (Chen et al., 2022): MuRAG uses a + • Reflexion (Shinn et al., 2023): Reflexion dense multimodal retriever that encodes both + proposes a verbal memory mechanism in queries and memory entries into a shared em- + which agents store self-generated reflections bedding space using a joint vision–language + as episodic memories. After each trial, feed- encoder. At inference time, it performs max- + back is converted into language reflections imum inner product search over an external + that are appended to memory and reused as memory to retrieve the most relevant items, + context to guide decisions, enabling trial-and- which are then used to augment generation. + error learning via memory accumulation. We use the retrieval paradigm in this paper for + our benchmark implementation. + • MemGPT (Packer et al., 2023): MemGPT + proposes an OS-inspired memory architec- • UniversalRAG (Yeo et al., 2025): It intro- + ture that treats the LLM context window as duces a modality- and granularity-aware RAG + limited working memory and manages long- framework that dynamically routes each query + term information via hierarchical external stor- to the most appropriate knowledge source be- + age. Through self-directed function calls, the fore retrieval. Instead of retrieving from a + unified corpus, it first predicts the required Face repositories8 . Then, we deploy them on + modality and granularity and then performs A100 GPUs under the Linux system with the + targeted retrieval within the selected corpus, vLLM engine9 . For closed-sourced MLLM mod- + reducing modality bias and retrieval noise. els, we access them via official APIs. The mem- + ory model implementation and reproduction are + • NGM (Fisher, 2025): It proposes Neural built based on the open-sourced memory frame- + Graph Memory, a graph-structured multi- work MemEngine10 (Zhang et al., 2025c). The to- + modal memory that stores episodic expe- tal API compute budget for open-sourced MLLM + riences as nodes enriched with modality- models was under one thousand US dollars. The + specific embeddings and temporal metadata. detailed running time for each memory model is + Memories are retrieved via graph traversal, en- illustrated in Figure 5. + abling associative, temporal, and cross-modal + recall beyond flat vector similarity, and sup- A.5.2 Benchmark Settings + porting long-horizon episodic reasoning. To eliminate the influence of backbone differences + and minimize randomness, all experimental set- + • AUGUSTUS (Jain et al., 2025): AUGUSTUS tings use the same MLLM backbone, with a fixed + introduces a cognition-inspired multimodal random seed and the temperature set to 0. To fur- + memory that organizes long-term memory ther ensure a fair comparison, for memory systems + into recall memory for raw conversation his- that involve embedding-based storage and retrieval, + tory and a hierarchical contextual memory for we uniformly adopt the widely used GME-Qwen2- + semantic concepts linked to multimodal con- VL-2B-Instruct (Zhang et al., 2024b) as the embed- + text. Abstracting user interactions into seman- ding model. For memory methods that require a + tic tags and retrieving information through a retriever, we adopt a default retrieval size of K=10. + concept-driven search enables efficient, per- For other hyperparameters of the models, we retain + sonalized, and long-horizon multimodal mem- their optimal settings from the original codebase. + ory access beyond flat vector databases. + A.5.3 Evaluation Pipeline + The corresponding papers and implementation + The overall evaluation pipeline is based on the +details of these methods are summarized in Table 7. + MemEngine (Zhang et al., 2025c) framework. The +For methods without publicly available implemen- + setup is illustrated in Appendix A.5.2. For baseline +tation code (marked as N/A), we re-implement + methods that involve calling an MLLM module, +them based on the methodological descriptions pro- + we consistently use the corresponding backbone +vided in the original papers if available. + MLLM of each method to ensure a fair comparison. +Table 7: The detailed resource list of models imple- Both the conversational data and the evaluation data +mented in our benchmark. are stored in JSON format. For conversational data, + following existing works, dialogue information is + Model Venue Paper Implement. Source streamed to each memory method at the granular- + Full Memory-Text N/A N/A Link ity of dialogue rounds. During question-answering + FIFO N/A N/A Link + NaiveRAG N/A N/A Link evaluation, the accumulated memory contents are + Gen. Agent UIST 2023 Link Link concatenated into the context, after which the cor- + Reflexion NeurIPS 2023 Link Link + MemGPT Arxiv 2023 Link Link responding MLLM is invoked to generate the fi- + A-Mem NeurIPS 2025 Link Link + MemoryOS EMNLP 2025 Link Link + nal answer and save the output. For tasks such as + Full Memory-Multimodal N/A N/A N/A visual-centric search, conflict detection, and answer + MuRAG EMNLP 2022 Link N/A + UniversalRAG Arxiv 2025 Link Link + refusal, which require specific output formats, ad- + NGM Arxiv 2025 Link Link ditional format-constrained prompts are appended + AUGUSTUS Arxiv 2025 Link N/A + after the question, as shown in Figure 9. Finally, + all generated answers are normalized in a unified + manner before metric computation. The prompt +A.5 Benchmark Evaluation Details + template used for LLM-as-a-Judge evaluation is +A.5.1 Experimental Environments 8 + https://huggingface.co +For open-sourced MLLM models, we obtain the of- 9 + https://github.com/vllm-project/vllm + 10 +ficial models from their corresponding Hugging https://github.com/nuster1128/MemEngine + illustrated in Figure 10. • 0: Incorrect or missing answer, including con- + tradictions or hallucinations. +A.5.4 Evaluation Metrics +We evaluate Mem-Gallery from three key perspec- • 0.25: Poor or tangential answer that touches +tives: question-answering performance, retrieval on the topic but misses the core entity or value. +effectiveness, and computational efficiency. All +metrics are computed at the instance level and then • 0.5: Partially correct answer that captures the +averaged over the QA set. main concept but lacks completeness or key + details. +Question-Answering Metrics. For answer qual- +ity, we adopt four widely used metrics in conversa- • 0.75: Largely correct answer with only minor +tional question answering. omissions or imprecision. + F1 measures token-level overlap between the +predicted answer Ap and the reference answer Ar . • 1: Correct and exact answer that fully matches +Let Tp and Tr denote the multisets of tokens in Ap the ground truth. +and Ar , respectively. Precision P and recall R are +defined as: The final LLM-as-a-Judge score is obtained by av- + eraging the assigned scores across evaluation in- + |Tp ∩ Tr | |Tp ∩ Tr | + P = , R= . (4) stances. This metric complements lexical overlap + |Tp | |Tr | metrics by capturing semantic equivalence, para- +The F1 score is then computed as: phrasing, and partial correctness. + For the Conflict Detection (CD) task, the model + 2P R is required to output a single binary token (Yes or + F1 = . (5) + P +R No). Since this is a binary classification task and + all evaluated models reliably follow the instruction + Exact Match (EM) evaluates whether the pre- format, the values of F1, EM, BLEU-1, and LLM- +dicted answer exactly matches the reference answer as-a-Judge coincide for this task. +after normalization (e.g., lowercasing and punctua- +tion removal): Retrieval Effectiveness Metrics. To assess mem- + ( ory access quality independently of answer gen- + 1, if Ap = Ar , eration, we adopt standard information retrieval + EM = (6) + 0, otherwise. metrics. For each evaluation query, let RK denote + the set of top-K retrieved memory entries, and G + BLEU-1 measures unigram-level precision be- denote the set of ground-truth relevant memory +tween the predicted answer Ap and the reference entries annotated as evidence clues. +answer Ar . Let countAp (w) denote the number of Recall@K is defined as: +occurrences of unigram w in Ap , and countAr (w) +denote its occurrences in Ar . The clipped count |RK ∩ G| + Recall@K = . (9) +c(w) is defined as: |G| + + Precision@K is defined as: +  + c(w) = min countAp (w), countAr (w) . (7) + +BLEU-1 is then computed as: |RK ∩ G| + Precision@K = . (10) + P |RK | + w∈Ap c(w) + BLEU-1 = P . (8) Hit@K measures whether at least one relevant + w∈Ap countAp (w) + memory entry is retrieved: + LLM-as-a-Judge evaluates semantic correct- ( +ness using Qwen-2.5-72B-Instruct as an automatic 1, if RK ∩ G ̸= ∅, + Hit@K = (11) +evaluator. Given the question, the ground-truth an- 0, otherwise. +swer, and the model prediction, the judge assigns +a discrete score from the set {0, 0.25, 0.5, 0.75, 1} All retrieval metrics are first computed per query +according to a predefined rubric. Specifically: and then averaged across the evaluation set. + Format Restriction Prompts + + Visual-centric Search: + Return the image_id of the image(s). If there are multiple images, sort them in ascending order and + separate them by commas. Format example: “D2:IMG_003, D2:IMG_010, D10:IMG_002” (for + format reference only). + Conflict Detection: + Please check whether this information conflicts with the conversation, and reply strictly with either + “Yes.” or “No.” + Answer Refusal: + Provide your answer based on the information in the conversation. Only if the information about + the question is not present in the conversation, reply with: “Not mentioned.” + + + Figure 9: Prompts for format restriction tasks. + + +Efficiency Metrics. We evaluate efficiency from memory performance across most tasks and meth- +a system-level perspective. For each model, we ods. This trend is visible when scaling from +measure: (1) Information Memorization Time, Qwen-2.5-VL-3B to Qwen-2.5-VL-7B, and further +defined as the cumulative wall-clock time spent from GPT-4.1-Nano to Gemini-2.5-Flash-Lite. The +processing and storing conversational inputs during gains are systematic rather than isolated, indicat- +interaction. (2) Memory Retrieval and Answer ing that increased backbone capacity enhances the +Generation Time, defined as the wall-clock time model’s ability to consume retrieved memory and +required to retrieve memory entries and generate generate coherent responses. However, these im- +the final response at evaluation time. All efficiency provements do not fundamentally alter which task +metrics are reported in seconds. categories remain difficult. Reasoning-intensive + tasks and knowledge management tasks continue +A.6 Additional Results and Analysis to exhibit lower performance relative to extraction- +A.6.1 MLLM Backbone Analysis oriented tasks, even under the strongest backbones. +To analyze how the choice of MLLM backbone This suggests that backbone scaling primarily im- +influences memory performance, we evaluate rep- proves memory utilization, rather than resolving +resentative open-source and closed-source MLLMs intrinsic limitations in long-horizon reasoning and +under a unified memory framework. Specifically, memory consistency of existing memory methods. +Table 8 and Table 3 report results on the open- Backbone scaling amplifies memory design +source Qwen-2.5-VL-3B and Qwen-2.5-VL-7B quality rather than compensating for it. Despite +backbones, while Table 9 and Table 10 report large differences in backbone capacity and model +results on the closed-source GPT-4.1-Nano and family, the relative ranking of memory methods +Gemini-2.5-Flash-Lite backbones. Moreover, Fig- remains largely stable across all evaluated settings. +ure 11, Figure 12, Figure 13, and Figure 14 illus- Well-organized memory systems consistently out- +trate the radar chart of three representative textual perform naïve or unstructured baselines under both +memory models (MemGPT, A-Mem, and Memo- weak and strong backbones, regardless of whether +ryOS) and three representative multimodal memory the memory is textual or multimodal. This stabil- +models (MuRAG, UniversalRAG, and AUGUS- ity indicates that backbone scaling does not com- +TUS) to clearly present the advantages and disad- pensate for poor memory organization. Instead, +vantages of different subtasks. Across all settings, stronger backbones tend to amplify existing dif- +the same set of textual and multimodal memory ferences between memory designs. Multimodal +methods is applied, enabling a controlled compari- memory benefits from stronger backbones only +son of backbone effects. when cross-modal alignment and retrieval quality + Overall effect of backbone capability. Across are sufficiently well controlled. Otherwise, strong +both open-source and closed-source backbones, textual memory can achieve comparable effective- +stronger MLLMs consistently improve absolute ness with lower complexity, such as the equipment + LLM-as-a-Judge Prompt + + You are an impartial judge evaluating the memory capabilities of an AI assistant with the question- + answering task. Your task is to compare the Assistant’s Answer against the Ground Truth and + assign a score of 0, 0.25, 0.5, 0.75, or 1. + Scoring Rubric + Score 0 (Incorrect / Miss): + - The answer contradicts the Ground Truth. + - For Yes/No questions: The answer has the wrong polarity (e.g., says "Yes" when Ground Truth is + "No"). + - For Open-ended questions: The answer provides factually wrong information or hallucinations. + - The assistant fails to provide the required information. + Score 0.25 (Poor / Tangential): + - The answer touches on the topic but misses the core entity or key value required. + - The answer contains a mix of minor correct details and significant hallucinations or wrong + associations. + - The answer is excessively vague to the point of being useless (e.g., answering "a dog" instead of + "a golden retriever"). + Score 0.5 (Partial / Vague): + - The answer is technically correct, but lacks confidence or is incomplete. + - The answer captures the main entity or concept correctly but misses a part of the required + supporting details. + - For Yes/No questions: The polarity is correct, but the reasoning is flawed (if have), or the assistant + is uncertain (e.g., "I think it might be Yes"). + - For Open-ended questions: The answer is too general or misses key adjectives/details present in + the Ground Truth. + Score 0.75 (Good / Minor Imperfection): + - The answer is largely accurate and captures the core information confidently. + - It misses only minor details (e.g., specific adjectives or secondary details) that do not alter the + main truth. + - The answer contains all the correct information but includes unnecessary "fluff" or slight conver- + sational filler that reduces precision. + Score 1 (Correct / Exact): + - The answer is accurate, precise, and confident. + - For Yes/No questions: The polarity matches the Ground Truth perfectly. + - For Open-ended questions: The answer contains all the core information and necessary details + required by the Ground Truth without hallucinations. + Input Data + Question: {question} + Ground Truth: {ground_truth} + Assistant Answer: {model_output} + Output Format + Output strictly in the following JSON format: "score": <0, 0.25, 0.5, 0.75, or 1>, "reasoning": + "" + + + Figure 10: The prompt of the LLM-as-a-Judge metric for Qwen-2.5-72B-Instruct. + + +with Gemini-2.5-Flash-Lite. stronger backbones. Although stronger back- + bones lead to consistent gains on reasoning- + Reasoning remains a bottleneck despite + MemGPT MemoryOS UniversalRAG MemGPT MemoryOS UniversalRAG + A-Mem MuRAG AUGUSTUS A-Mem MuRAG AUGUSTUS + F1 BLEU-1 F1 BLEU-1 + FR FR FR FR + AR 1.0 VS AR 1.0 VS AR 1.0 VS AR 1.0 VS + 0.8 0.8 0.8 0.8 + 0.6 0.6 0.6 0.6 + 0.4 0.4 0.4 0.4 +CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL + 0.0 0.0 0.0 0.0 + + + + KR TR KR TR KR TR KR TR + + MR VR MR VR MR VR MR VR + + EM LLM-as-a-Judge EM LLM-as-a-Judge + FR FR FR FR + AR 1.0 VS AR 1.0 VS AR 1.0 VS AR 1.0 VS + 0.8 0.8 0.8 0.8 + 0.6 0.6 0.6 0.6 + 0.4 0.4 0.4 0.4 +CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL + 0.0 0.0 0.0 0.0 + + + + KR TR KR TR KR TR KR TR + + MR VR MR VR MR VR MR VR + + +Figure 11: Radar chart of subtask performance for rep- Figure 13: Radar chart of subtask performance for rep- +resentative memory models on Qwen-2.5-VL-3B. resentative memory models on GPT-4.1-Nano. + + MemGPT MemoryOS UniversalRAG MemGPT MemoryOS UniversalRAG + A-Mem MuRAG AUGUSTUS A-Mem MuRAG AUGUSTUS + F1 BLEU-1 F1 BLEU-1 + FR FR FR FR + AR 1.0 VS AR 1.0 VS AR 1.0 VS AR 1.0 VS + 0.8 0.8 0.8 0.8 + 0.6 0.6 0.6 0.6 + 0.4 0.4 0.4 0.4 +CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL + 0.0 0.0 0.0 0.0 + + + + KR TR KR TR KR TR KR TR + + MR VR MR VR MR VR MR VR + + EM LLM-as-a-Judge EM LLM-as-a-Judge + FR FR FR FR + AR 1.0 VS AR 1.0 VS AR 1.0 VS AR 1.0 VS + 0.8 0.8 0.8 0.8 + 0.6 0.6 0.6 0.6 + 0.4 0.4 0.4 0.4 +CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL CD 0.2 TTL + 0.0 0.0 0.0 0.0 + + + + KR TR KR TR KR TR KR TR + + MR VR MR VR MR VR MR VR + + +Figure 12: Radar chart of subtask performance for rep- Figure 14: Radar chart of subtask performance for rep- +resentative memory models on Qwen-2.5-VL-7B. resentative memory models on Gemini-2.5-Flash-Lite. + + +oriented tasks, including temporal reasoning, tual memory methods remain competitive with mul- +visual-centric reasoning, and multi-entity reason- timodal approaches. These observations indicate +ing, the magnitude of these gains is noticeably that long-horizon reasoning over multimodal mem- +smaller than those observed in extraction-focused ory is not primarily constrained by backbone capac- +tasks. This pattern is consistent across both open- ity, but rather by how multimodal information is +source and closed-source backbones. In several structured, linked, and jointly reasoned over within +reasoning subtasks, the performance gap between the memory system. These results indicate that the +textual and multimodal memory narrows as back- organization and maintenance of multimodal mem- +bone capability increases, and well-designed tex- ory mechanisms remain an open and promising + direction for future research. 0.70 0.50 + + 0.65 + Limited sensitivity in knowledge management 0.60 + 0.45 + Gen. Agent + NaiveRAG +tasks. For knowledge management tasks, includ- 0.55 + 0.40 A-Mem + MemoryOS + + + + + EM + F1 + 0.35 MuRAG +ing knowledge resolution, conflict detection, and 0.50 UniversalRAG + NGM + 0.45 0.30 AUGUSTUS +answer refusal, improvements from stronger back- 0.40 + 0.25 +bones are relatively limited across all evaluated 0.35 + 5 10 15 20 5 10 15 20 + K K +MLLMs. Both open-source and closed-source +backbones exhibit similar qualitative behavior, with Figure 15: Parameter study on the retrieval size K. +only marginal performance differences attributable +to backbone choice. This suggests diminishing +returns from backbone scaling for safety- and modal memory methods. While larger retrieval +consistency-oriented memory behaviors, which sizes improve coverage of relevant multimodal en- +rely more heavily on explicit memory state track- tries, they also introduce additional redundant or +ing, conflict awareness, and decision policies than weakly aligned visual–textual information. As a re- +on raw generative or perceptual capacity. sult, the benefits of higher recall are partially offset + by increased noise in the retrieved memory, limit- +A.6.2 Memory Retrieval Analysis ing the effectiveness of downstream reasoning and +This section analyzes how the number of re- answer generation. In contrast, moderate retrieval +trieved memory entries affects both retrieval effec- sizes such as K=10, as reflected in Table 3, appear +tiveness and downstream task performance. Ta- to offer a better balance between relevance and +ble 11, Table 3, Table 12, and Table 13 report redundancy for multimodal memory utilization. +task-level QA results under different retrieval sizes For reasoning-oriented tasks, including tempo- +K ∈ {5, 10, 15, 20}. Figure 15 shows the over- ral reasoning, visual-centric reasoning, and multi- +all task performance trend under K value changes. entity reasoning, the impact of retrieval size is even +Based on the fine-grained annotated clues for each more constrained. Performance differences across +evaluation data, Table 14 summarizes the corre- K=5, K=10, K=15, and K=20 are relatively small, +sponding retrieval metrics, including Recall@K, suggesting that reasoning performance is less sen- +Precision@K, and Hit@K for retrieval behavior sitive to retrieval quantity and more dependent on +analysis. All results are evaluated on the Qwen-2.5- how retrieved memory is structured and integrated. +VL-7B backbone. This observation holds consistently across both tex- + Impact of Retrieval Size on Downstream Task tual and multimodal memory methods. +Performance. The results in Table 11, Table 3, Knowledge management tasks, such as knowl- +Table 12, and Table 13 provide a continuous view edge resolution, conflict detection, and answer re- +of how retrieval quantity affects memory utiliza- fusal, exhibit the weakest sensitivity to retrieval +tion and task performance. Across most memory size. Across all values of K, task performance +models and task categories, we generally observe a remains relatively stable, indicating that simply +non-monotonic relationship between retrieval size retrieving more memory entries is insufficient to +and downstream performance. Increasing the re- improve consistency-oriented behaviors and knowl- +trieval size from a small value (K=5) to a moderate edge management capability. +range (K=10 or K=15) generally leads to consis- Overall, these results suggest that moderate re- +tent improvements, especially for memory extrac- trieval sizes, exemplified by K=10, often provide +tion and adaptation tasks such as factual retrieval, the most effective trade-off between retrieval cov- +visual-centric search, and test-time learning. erage and memory usability. Increasing retrieval + However, further increasing the retrieval size to size beyond this range yields diminishing returns, +K=20 does not consistently yield additional gains. particularly for multimodal memory systems, high- +As shown in Table 13, performance often satu- lighting the importance of selective and relevance- +rates or fluctuates across tasks, and in some cases aware retrieval rather than indiscriminate scaling +slightly degrades compared to the K=10 or K=15 of retrieved memory entries. +settings. This pattern indicates that retrieving more Retrieval Effectiveness versus Memory Uti- +memory entries beyond a moderate threshold does lization. Table 14 provides further insight into why +not necessarily translate into better task outcomes. increasing retrieval size does not consistently im- +This effect is particularly pronounced for multi- prove downstream task performance observed in + Table 8: Evaluation results on Qwen-2.5-VL-3B. The best and second-performed memory model(s) are highlighted +with orange and blue backgrounds. + + Qwen-2.5-VL-3B Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2501 0.1578 0.5664 0.2301 0.2467 0.4171 0.5985 0.5889 0.2282 0.6504 0.6601 0.6045 0.5660 + BLEU-1 0.2000 0.1138 0.4862 0.1847 0.1976 0.3494 0.4937 0.4897 0.1795 0.5673 0.5811 0.5290 0.4868 + FR EM 0.0959 0.0502 0.2740 0.0868 0.0959 0.1872 0.2843 0.3014 0.0868 0.3699 0.3699 0.3470 0.3105 + LLM-Judge 0.3105 0.1918 0.7443 0.2922 0.3082 0.5845 0.7690 0.7694 0.2717 0.8584 0.8516 0.7694 0.7626 +Extract. & Adapt. + + + + + F1 0.2687 0.1175 0.7424 0.3244 0.2691 0.2631 0.7653 0.7771 0.2154 0.8661 0.8395 0.8493 0.8537 + BLEU-1 0.2466 0.1012 0.6934 0.2826 0.2471 0.2381 0.7096 0.7123 0.1959 0.8337 0.8056 0.8066 0.8047 + VS EM 0.1569 0.0359 0.5033 0.1634 0.1601 0.1176 0.5802 0.5458 0.0980 0.6307 0.6275 0.6569 0.6242 + LLM-Judge 0.2337 0.0801 0.7092 0.2696 0.2337 0.2843 0.6794 0.7565 0.1797 0.8676 0.8350 0.8284 0.8464 + F1 0.4657 0.3640 0.6769 0.5454 0.4633 0.4833 0.6448 0.6595 0.4857 0.7966 0.7227 0.7732 0.7328 + BLEU-1 0.4279 0.3161 0.6253 0.4845 0.4251 0.4427 0.5884 0.5997 0.4397 0.7402 0.6655 0.7145 0.6803 + TTL EM 0.3294 0.2344 0.5104 0.3769 0.3234 0.3620 0.4927 0.4777 0.3353 0.6261 0.5668 0.6083 0.5786 + LLM-Judge 0.6365 0.5415 0.7433 0.6706 0.6380 0.6276 0.7080 0.7567 0.6320 0.8338 0.7982 0.8294 0.8027 + F1 0.2866 0.2398 0.4785 0.3288 0.2866 0.3996 0.6036 0.4942 0.2972 0.5700 0.5484 0.5332 0.5422 + BLEU-1 0.2688 0.2226 0.4454 0.3036 0.2683 0.3728 0.5817 0.4654 0.2807 0.5369 0.5119 0.4986 0.5072 + TR EM 0.1301 0.0976 0.3089 0.1626 0.1301 0.2114 0.4569 0.3089 0.1382 0.3984 0.3821 0.3659 0.3740 + LLM-Judge 0.3252 0.2480 0.6667 0.3821 0.3211 0.5813 0.6940 0.6707 0.3130 0.7602 0.7886 0.6951 0.7561 + F1 0.2545 0.1532 0.2966 0.1953 0.2552 0.3226 0.3441 0.3860 0.2285 0.4440 0.4554 0.4260 0.4681 +Reasoning + + + + + BLEU-1 0.2398 0.1384 0.2778 0.1823 0.2395 0.3065 0.3319 0.3685 0.2176 0.4230 0.4352 0.4100 0.4504 + VR EM 0.2011 0.1092 0.1897 0.1379 0.2011 0.2414 0.2806 0.2931 0.1839 0.3333 0.3333 0.3333 0.3563 + LLM-Judge 0.3161 0.1810 0.3822 0.2615 0.3190 0.4310 0.4101 0.5172 0.2615 0.5374 0.5489 0.5201 0.5690 + F1 0.2600 0.1934 0.4380 0.2683 0.2615 0.3400 0.4401 0.4483 0.2400 0.4851 0.4908 0.4697 0.4773 + BLEU-1 0.1943 0.1364 0.3277 0.1997 0.1969 0.2599 0.3162 0.3474 0.1787 0.3736 0.3788 0.3602 0.3639 + MR EM 0.0291 0.0243 0.0825 0.0388 0.0291 0.0340 0.0722 0.0971 0.0194 0.0874 0.0922 0.0874 0.1019 + LLM-Judge 0.4442 0.2985 0.7670 0.4345 0.4442 0.6092 0.7474 0.7597 0.4223 0.8083 0.8301 0.7985 0.7840 + F1 0.2034 0.1563 0.3382 0.2613 0.2061 0.2372 0.4280 0.3706 0.2007 0.3975 0.3883 0.3441 0.3604 + BLEU-1 0.1627 0.1118 0.2754 0.2085 0.1639 0.1869 0.3693 0.3094 0.1625 0.3358 0.3323 0.2842 0.2987 + KR EM 0.0741 0.0370 0.1111 0.0864 0.0741 0.0741 0.2267 0.1235 0.0864 0.1481 0.1728 0.1358 0.1235 +Knowledge Management + + + + + LLM-Judge 0.4012 0.2963 0.5926 0.4012 0.3951 0.5185 0.6600 0.6852 0.3704 0.6790 0.6852 0.6420 0.6914 + F1 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 + BLEU-1 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 + CD EM 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 + LLM-Judge 0.3333 0.3580 0.3210 0.3210 0.3333 0.3580 0.3200 0.2963 0.3457 0.3086 0.3086 0.3086 0.3333 + F1 0.9742 0.9783 0.8942 0.9578 0.9742 0.8387 0.9080 0.8992 0.9647 0.8823 0.8661 0.8707 0.8553 + BLEU-1 0.9730 0.9783 0.8924 0.9571 0.9730 0.8355 0.9080 0.8982 0.9628 0.8811 0.8648 0.8702 0.8540 + AR EM 0.9728 0.9783 0.8913 0.9565 0.9728 0.8315 0.9080 0.8967 0.9620 0.8804 0.8641 0.8696 0.8533 + LLM-Judge 0.9620 0.9620 0.8804 0.9429 0.9620 0.8234 0.9052 0.8804 0.9484 0.8641 0.8478 0.8587 0.8424 + F1 0.3798 0.2986 0.5832 0.4013 0.3793 0.4165 0.6059 0.6013 0.3665 0.6679 0.6479 0.6443 0.6383 + BLEU-1 0.3492 0.2689 0.5333 0.3620 0.3487 0.3794 0.5505 0.5462 0.3360 0.6194 0.5995 0.5953 0.5881 + Overall EM 0.2624 0.2040 0.3933 0.2671 0.2618 0.2700 0.4303 0.4103 0.2496 0.4728 0.4594 0.4670 0.4541 + LLM-Judge 0.4541 0.3521 0.6856 0.4597 0.4538 0.5383 0.6886 0.7162 0.4272 0.7756 0.7662 0.7463 0.7528 + + + + +Table 3 and Table 11-13. As the retrieval size K in Table 13 often saturates or fluctuates relative to +increases, retrieval effectiveness metrics exhibit a Table 3 and Table 12. In other words, higher re- +clear and expected trend: Recall@K and Hit@K call does not guarantee better memory utilization. +consistently improve across all retrievers, indicat- The additional retrieved entries do not consistently +ing that more relevant memory entries are success- contribute useful evidence for reasoning or answer +fully retrieved. This trend holds for both textual generation, and may instead distract the model dur- +and multimodal retrievers, and is especially pro- ing inference. +nounced for multimodal methods such as MuRAG + The effect is more evident for multimodal +and UniversalRAG, which achieve high recall even + memory systems. Compared to textual memory, +at moderate K value. + multimodal retrieval introduces heterogeneous vi- + sual–textual information, making it more sensitive + However, this improvement in recall is accom- + to redundancy and misalignment. Consequently, +panied by a systematic decline in Precision@K, + multimodal methods benefit most from moderate +particularly as K increases beyond moderate val- + retrieval sizes that balance recall and precision, as +ues. While larger K values ensure broader cover- + exemplified by the strong performance at K=10. +age of relevant memory entries, they also introduce + Beyond this range, further recall gains are offset by +a growing number of weakly relevant or redundant + increased retrieval noise, limiting improvements in +items. As a result, the retrieved memory becomes + downstream tasks. +increasingly noisy, especially in multimodal set- +tings where cross-modal alignment is imperfect. Overall, these results indicate that retrieval qual- +This divergence explains the observed decoupling ity, rather than retrieval quantity, is the domi- +between retrieval effectiveness and downstream nant factor governing downstream memory per- +task performance. Although Recall@K continues formance. Simply scaling the retrieval size im- +to improve from K=10 to K=20, task performance proves recall metrics but does not necessarily im- + Table 9: Evaluation results on GPT-4.1-Nano. The best and second-performed memory model(s) are highlighted +with orange and blue backgrounds. + + GPT-4.1-Nano Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2279 0.1379 0.5498 0.1672 0.2277 0.5983 0.6048 0.5848 0.1920 0.6256 0.6060 0.5922 0.5666 + BLEU-1 0.1724 0.0907 0.4682 0.1164 0.1719 0.5193 0.5194 0.4936 0.1413 0.5392 0.5189 0.5036 0.4734 + FR EM 0.0685 0.0274 0.2374 0.0228 0.0594 0.2740 0.2648 0.2466 0.0548 0.2785 0.2511 0.2785 0.2466 + LLM-Judge 0.2260 0.0936 0.7740 0.1918 0.2283 0.8082 0.8151 0.8425 0.1872 0.8653 0.8356 0.7968 0.7922 +Extract. & Adapt. + + + + + F1 0.2330 0.0839 0.7457 0.1787 0.2356 0.6853 0.7965 0.7639 0.1318 0.8478 0.8410 0.8329 0.8354 + BLEU-1 0.2175 0.0689 0.7021 0.1507 0.2210 0.6573 0.7553 0.6962 0.1155 0.8092 0.8109 0.7906 0.7831 + VS EM 0.1732 0.0294 0.5294 0.0980 0.1732 0.5392 0.5882 0.5621 0.0588 0.6275 0.6340 0.6340 0.6373 + LLM-Judge 0.2124 0.0507 0.6944 0.1585 0.2124 0.6536 0.7761 0.7255 0.0997 0.8431 0.8252 0.8056 0.8121 + F1 0.2278 0.1473 0.3618 0.2021 0.2226 0.2866 0.3279 0.3263 0.1991 0.4395 0.4407 0.5273 0.4488 + BLEU-1 0.1609 0.0974 0.2523 0.1414 0.1567 0.1969 0.2232 0.2346 0.1406 0.3468 0.3581 0.4372 0.3491 + TTL EM 0.0504 0.0148 0.0504 0.0297 0.0445 0.0386 0.0326 0.0742 0.0356 0.1662 0.1958 0.2641 0.1424 + LLM-Judge 0.5742 0.4585 0.7834 0.5801 0.5579 0.6588 0.8027 0.7389 0.5015 0.7804 0.8012 0.8709 0.8145 + F1 0.2035 0.1356 0.4381 0.2028 0.1997 0.4306 0.5116 0.5017 0.1916 0.5007 0.4366 0.4924 0.4808 + BLEU-1 0.1744 0.1143 0.4032 0.1746 0.1694 0.3932 0.4730 0.4649 0.1682 0.4676 0.3997 0.4606 0.4484 + TR EM 0.0569 0.0488 0.2764 0.0894 0.0650 0.2520 0.3415 0.3171 0.0650 0.3252 0.2683 0.3496 0.3252 + LLM-Judge 0.2764 0.1382 0.7154 0.2886 0.2846 0.7276 0.7846 0.8130 0.2398 0.7520 0.7480 0.7236 0.7154 + F1 0.2011 0.0714 0.2041 0.0987 0.2000 0.4140 0.3269 0.3697 0.0909 0.3426 0.4417 0.3043 0.3817 +Reasoning + + + + + BLEU-1 0.1796 0.0491 0.1791 0.0747 0.1772 0.3855 0.2979 0.3431 0.0690 0.3077 0.4146 0.2738 0.3518 + VR EM 0.1379 0.0172 0.0862 0.0172 0.1322 0.2931 0.1954 0.2586 0.0345 0.2069 0.3218 0.1724 0.2529 + LLM-Judge 0.3276 0.1379 0.3736 0.1810 0.3305 0.6207 0.5661 0.5862 0.2011 0.5575 0.6638 0.4713 0.5747 + F1 0.2415 0.1814 0.4490 0.2354 0.2389 0.4428 0.4669 0.4523 0.2017 0.5024 0.4670 0.4570 0.4829 + BLEU-1 0.1720 0.1174 0.3402 0.1704 0.1710 0.3388 0.3602 0.3479 0.1385 0.3946 0.3670 0.3506 0.3798 + MR EM 0.0194 0.0097 0.0388 0.0097 0.0146 0.0388 0.0437 0.0583 0.0097 0.0534 0.0534 0.0534 0.0680 + LLM-Judge 0.2451 0.0922 0.7985 0.2597 0.2500 0.7985 0.8617 0.8325 0.1942 0.8617 0.7816 0.8180 0.8180 + F1 0.1454 0.1001 0.2852 0.1408 0.1411 0.3733 0.3283 0.2873 0.1293 0.3626 0.2972 0.2878 0.2607 + BLEU-1 0.0965 0.0633 0.2174 0.0960 0.0934 0.3017 0.2556 0.2190 0.0902 0.2897 0.2316 0.2252 0.1958 + KR EM 0.0123 0.0000 0.0494 0.0000 0.0123 0.1111 0.0741 0.0370 0.0123 0.0988 0.0494 0.0864 0.0617 +Knowledge Management + + + + + LLM-Judge 0.2840 0.1975 0.6049 0.2407 0.2963 0.7593 0.6852 0.6790 0.2531 0.7469 0.6543 0.6173 0.5741 + F1 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 + BLEU-1 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 + CD EM 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 + LLM-Judge 0.3704 0.3704 0.4321 0.4074 0.3704 0.3951 0.4321 0.4444 0.3704 0.4074 0.3827 0.4321 0.3951 + F1 0.9682 0.9891 0.8406 0.9751 0.9737 0.7911 0.8496 0.8874 0.9791 0.9039 0.9102 0.9382 0.9058 + BLEU-1 0.9679 0.9891 0.8323 0.9738 0.9733 0.7811 0.8430 0.8831 0.9788 0.9003 0.9058 0.9335 0.9009 + AR EM 0.9674 0.9891 0.8261 0.9728 0.9728 0.7717 0.8370 0.8804 0.9783 0.8967 0.9022 0.9293 0.8967 + LLM-Judge 0.9565 0.9728 0.8587 0.9647 0.9592 0.8370 0.8668 0.8940 0.9620 0.9022 0.9103 0.9321 0.9022 + F1 0.3084 0.2291 0.5057 0.2769 0.3075 0.5034 0.5380 0.5339 0.2636 0.5832 0.5774 0.5849 0.5703 + BLEU-1 0.2703 0.1974 0.4436 0.2389 0.2698 0.4480 0.4764 0.4705 0.2293 0.5243 0.5236 0.5266 0.5080 + Overall EM 0.1923 0.1420 0.2800 0.1596 0.1899 0.2987 0.3092 0.3203 0.1572 0.3518 0.3600 0.3746 0.3489 + LLM-Judge 0.3966 0.2779 0.7046 0.3720 0.3960 0.7063 0.7651 0.7507 0.3346 0.7814 0.7747 0.7659 0.7583 + + + + +prove task outcomes. Effective multimodal mem- Figure 16 presents a test-time learning scenario +ory systems therefore require selective, relevance- in which the agent must adapt to newly introduced +aware retrieval strategies that prioritize precision visual exemplars during the conversation and gen- +and alignment over exhaustive retrieval. Therefore, eralize the learned concept at inference time. The +this highlights the need for future work to consider example requires identifying the clothing category +the principle that “less but high-quality is more” in based on a sequence of visual demonstrations and +multimodal memory design (Chen et al., 2025). prior conversational cues. Although textual mem- + ory baselines are augmented with high-quality vi- +A.7 Case Study + sual captions, textual memory methods still exhibit +We further present a qualitative case study to illus- clear limitations. Although NaiveRAG and A-Mem +trate how representative memory systems behave retrieve relevant textual clues, they lack the ability +under different multimodal long-term memory chal- to integrate newly observed visual patterns, result- +lenges in Mem-Gallery. We select three textual ing in generic or incorrect predictions. MemoryOS +memory models (NaiveRAG, A-Mem, MemoryOS) exhibits a similar limitation, indicating that strong +and three multimodal memory models (MuRAG, textual organization alone is insufficient when the +UniversalRAG, AUGUSTUS) and analyze their re- task requires visual abstraction at inference time. +sponses on three representative subtasks. These In contrast, multimodal memory methods that ex- +cases correspond to test-time learning of the mem- plicitly preserve visual representations demonstrate +ory extraction & adaptation task, visual-centric rea- stronger adaptability. MuRAG and AUGUSTUS +soning of the memory reasoning task, and knowl- successfully align the new image with previously +edge resolution of the memory knowledge manage- observed visual examples and correctly infer the +ment task, as shown in Figure 16, Figure 17, and category. This case highlights the limitation of +Figure 18, respectively. From the case studies, we caption-based textual memory. While high-quality +can have the following observations. + Table 10: Evaluation results on Gemini-2.5-Flash-Lite. The best and second-performed memory model(s) are +highlighted with orange and blue backgrounds. + + Gemini-2.5-Flash-Lite Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2014 0.1014 0.5905 0.2329 0.2014 0.6667 0.5266 0.6622 0.1534 0.6888 0.7014 0.6341 0.6011 + BLEU-1 0.1652 0.0732 0.4999 0.1802 0.1652 0.5633 0.4270 0.5683 0.1214 0.6009 0.6136 0.5510 0.5141 + FR EM 0.0822 0.0320 0.2648 0.0868 0.0822 0.2557 0.1690 0.3425 0.0594 0.3653 0.3562 0.3333 0.3151 + LLM-Judge 0.2237 0.0868 0.7922 0.2945 0.2215 0.9475 0.8402 0.8562 0.1507 0.8927 0.9064 0.8082 0.7922 +Extract. & Adapt. + + + + + F1 0.2252 0.0598 0.7909 0.3450 0.2252 0.9204 0.8451 0.8427 0.1051 0.9077 0.9175 0.8753 0.8811 + BLEU-1 0.2102 0.0553 0.7539 0.3156 0.2102 0.8957 0.8019 0.7918 0.0929 0.8837 0.8919 0.8456 0.8443 + VS EM 0.1863 0.0327 0.6078 0.2451 0.1863 0.8105 0.6797 0.6536 0.0654 0.7909 0.7745 0.7484 0.7157 + LLM-Judge 0.2141 0.0458 0.7663 0.3170 0.2141 0.9265 0.8415 0.8350 0.0931 0.9052 0.9199 0.8611 0.8775 + F1 0.4697 0.2002 0.7839 0.6149 0.4697 0.8517 0.7173 0.8360 0.3031 0.8750 0.8464 0.8477 0.8549 + BLEU-1 0.4322 0.1260 0.7259 0.5552 0.4322 0.8012 0.6229 0.7709 0.2654 0.8130 0.7689 0.7745 0.7908 + TTL EM 0.3501 0.0712 0.6113 0.4481 0.3501 0.6795 0.4243 0.6380 0.1958 0.6677 0.6410 0.6350 0.6499 + LLM-Judge 0.5341 0.2507 0.8680 0.6869 0.5341 0.9021 0.9006 0.9021 0.3442 0.9258 0.8976 0.9184 0.9139 + F1 0.2105 0.1447 0.4801 0.2201 0.2105 0.5475 0.4714 0.5325 0.1796 0.5806 0.5675 0.5280 0.5555 + BLEU-1 0.1953 0.1345 0.4513 0.2046 0.1953 0.5071 0.4301 0.5047 0.1637 0.5507 0.5392 0.5011 0.5267 + TR EM 0.0976 0.0407 0.3333 0.0732 0.0976 0.3496 0.2927 0.3821 0.0650 0.4309 0.4146 0.3984 0.4065 + LLM-Judge 0.2317 0.1423 0.6870 0.2846 0.2317 0.8902 0.7967 0.7358 0.1911 0.7886 0.8089 0.6992 0.7886 + F1 0.1909 0.0423 0.3135 0.1466 0.1909 0.6294 0.4262 0.3997 0.0798 0.4470 0.4954 0.4422 0.4533 +Reasoning + + + + + BLEU-1 0.1819 0.0356 0.2985 0.1398 0.1819 0.6083 0.4075 0.3843 0.0707 0.4267 0.4785 0.4200 0.4325 + VR EM 0.1494 0.0230 0.2241 0.1092 0.1494 0.5230 0.3276 0.3103 0.0517 0.3563 0.4023 0.3563 0.3563 + LLM-Judge 0.2443 0.0402 0.4138 0.2443 0.2443 0.7845 0.5603 0.5805 0.1063 0.6264 0.6782 0.5920 0.5718 + F1 0.1648 0.0834 0.4521 0.1765 0.1648 0.5210 0.4529 0.5161 0.1268 0.5137 0.5137 0.4783 0.4691 + BLEU-1 0.1250 0.0594 0.3508 0.1265 0.1250 0.4130 0.3553 0.4111 0.0987 0.4032 0.4011 0.3703 0.3575 + MR EM 0.0097 0.0097 0.0631 0.0097 0.0097 0.0680 0.0388 0.0825 0.0097 0.0680 0.0728 0.0437 0.0631 + LLM-Judge 0.2184 0.0922 0.7767 0.2743 0.2184 0.9272 0.8519 0.8568 0.1481 0.8714 0.8738 0.8058 0.7864 + F1 0.1157 0.0604 0.2813 0.1050 0.1157 0.3915 0.3341 0.3609 0.0918 0.4037 0.3710 0.3111 0.3168 + BLEU-1 0.0848 0.0370 0.2177 0.0810 0.0848 0.3225 0.2644 0.2956 0.0673 0.3534 0.3156 0.2607 0.2631 + KR EM 0.0247 0.0000 0.0864 0.0247 0.0247 0.1605 0.0988 0.1605 0.0247 0.1975 0.1481 0.1358 0.1235 +Knowledge Management + + + + + LLM-Judge 0.2531 0.1296 0.6235 0.2037 0.2531 0.8025 0.7593 0.7840 0.1728 0.7716 0.7222 0.5988 0.6667 + F1 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.4938 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 + BLEU-1 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.4938 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 + CD EM 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.4938 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 + LLM-Judge 0.3951 0.3827 0.5185 0.4198 0.3951 0.5556 0.5123 0.6049 0.3827 0.5309 0.5432 0.6296 0.5679 + F1 0.9644 0.9946 0.9122 0.9688 0.9644 0.8512 0.9175 0.9220 0.9844 0.9242 0.9186 0.9332 0.9339 + BLEU-1 0.9634 0.9946 0.9102 0.9682 0.9634 0.8454 0.9155 0.9205 0.9841 0.9218 0.9162 0.9318 0.9321 + AR EM 0.9620 0.9946 0.9076 0.9674 0.9620 0.8370 0.9130 0.9185 0.9837 0.9185 0.9130 0.9293 0.9293 + LLM-Judge 0.9511 0.9783 0.8995 0.9592 0.9511 0.8478 0.9212 0.9212 0.9674 0.9130 0.9076 0.9212 0.9239 + F1 0.3408 0.2158 0.6282 0.3936 0.3408 0.7202 0.6294 0.6861 0.2627 0.7155 0.7157 0.6901 0.6877 + BLEU-1 0.3178 0.1913 0.5796 0.3608 0.3178 0.6706 0.5702 0.6327 0.2424 0.6676 0.6644 0.6400 0.6370 + Overall EM 0.2595 0.1555 0.4436 0.2858 0.2595 0.5219 0.4120 0.4904 0.1940 0.5283 0.5207 0.5079 0.5020 + LLM-Judge 0.3729 0.2236 0.7452 0.4407 0.3726 0.8755 0.8115 0.8165 0.2764 0.8437 0.8472 0.8030 0.8057 + + + + +captions can convey semantic details, they remain the retrieved textual clue and the visual evidence +a lossy and static representation of visual infor- to reach the correct conclusion. This case exposes +mation, making it difficult for models to perform a critical gap in existing memory systems, namely +visual pattern induction and concept generalization that multimodal reasoning requires not only retriev- +at test time. Effective test-time learning therefore ing relevant information but also jointly reasoning +requires memory systems that can retain and com- over visual and textual cues in a coherent manner. +pare visual evidence directly, rather than relying Figure 18 focuses on knowledge resolution under +solely on textualized visual descriptions. evolving conversational states. In this example, the + Figure 17 illustrates a visual-centric reasoning user explicitly corrects earlier information about +task, where the model must determine whether the a preferred dog breed. The model must update its +dog in a given image matches the breed of a pet memory and discard outdated knowledge. Most +mentioned earlier in the conversation. This case evaluated methods struggle with this scenario. This +reveals a more subtle failure mode. Several models example reveals two distinct failure modes. First, +retrieve the correct conversational evidence, such some memory systems fail to update their knowl- +as the mention that Amy owns a Cairn Terrier, yet edge, continuing to rely on outdated beliefs despite +still produce incorrect final answers. In particular, the explicit correction in the dialogue. This be- +UniversalRAG demonstrates correct retrieval but havior is particularly evident in some multimodal +flawed reasoning, incorrectly concluding that the memory methods, indicating that preserving mul- +dog in the image is not a Cairn Terrier despite vi- timodal information alone does not guarantee ef- +sual similarity. This indicates that retrieval success fective knowledge revision. Second, even when +does not guarantee correct multimodal reasoning. some textual memory models successfully incor- +Textual memory models, on the other hand, of- porate the updated preference at the textual level, +ten fail earlier due to missing or incomplete visual they still fail to produce the correct answer due +grounding. Only MuRAG correctly integrates both to insufficient visual grounding. Lacking the abil- + Table 11: Benchmark performance results on Qwen-2.5-VL-7B with the retrieval number K=5. The best and +second-performed memory model(s) are highlighted with orange and blue backgrounds. + + Qwen-2.5-VL-7B (K=5) Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2376 0.1075 0.5293 0.2216 0.2391 0.5928 0.5562 0.6045 0.2150 0.6291 0.6321 0.5536 0.5745 + BLEU-1 0.1865 0.0683 0.4531 0.1684 0.1903 0.5098 0.4668 0.5229 0.1626 0.5440 0.5464 0.4695 0.4870 + FR EM 0.0913 0.0228 0.2785 0.0685 0.0913 0.3288 0.2603 0.3288 0.0685 0.3516 0.3379 0.3105 0.2968 + LLM-Judge 0.2626 0.0662 0.7146 0.2580 0.2626 0.8539 0.7100 0.7877 0.2260 0.8105 0.8059 0.7123 0.7489 +Extract. & Adapt. + + + + + F1 0.1992 0.0398 0.6864 0.2416 0.1954 0.6239 0.7366 0.7614 0.1658 0.8664 0.8740 0.7847 0.8301 + BLEU-1 0.1873 0.0322 0.6307 0.2082 0.1840 0.5834 0.6596 0.7067 0.1473 0.8301 0.8387 0.7417 0.7731 + VS EM 0.1601 0.0131 0.4967 0.1634 0.1569 0.4118 0.5719 0.5817 0.1078 0.6928 0.6993 0.6340 0.6569 + LLM-Judge 0.1961 0.0343 0.6650 0.2288 0.1895 0.5964 0.7059 0.7418 0.1683 0.8725 0.8627 0.7778 0.8235 + F1 0.4500 0.3240 0.5938 0.4731 0.4486 0.2924 0.6882 0.5512 0.4147 0.8161 0.7853 0.7706 0.7631 + BLEU-1 0.3799 0.2557 0.5170 0.3980 0.3798 0.2295 0.6261 0.4759 0.3477 0.7464 0.7068 0.7027 0.6810 + TTL EM 0.2374 0.0742 0.3858 0.2493 0.2374 0.1009 0.5134 0.3650 0.2107 0.6024 0.5846 0.6053 0.5519 + LLM-Judge 0.7092 0.6424 0.8353 0.7626 0.7033 0.7092 0.8220 0.7774 0.7107 0.9021 0.8620 0.8887 0.8754 + F1 0.2545 0.1484 0.4088 0.2017 0.2553 0.5661 0.5235 0.5598 0.2294 0.5439 0.5233 0.5199 0.5779 + BLEU-1 0.2363 0.1305 0.3872 0.1836 0.2363 0.5326 0.4959 0.5306 0.2065 0.5211 0.4955 0.4952 0.5475 + TR EM 0.1545 0.0732 0.3089 0.1138 0.1545 0.3496 0.4065 0.3902 0.1382 0.4146 0.3740 0.3984 0.4309 + LLM-Judge 0.2805 0.1341 0.5325 0.2276 0.2764 0.8008 0.6626 0.7276 0.2480 0.6707 0.6870 0.6463 0.7317 + F1 0.2552 0.0900 0.2711 0.1669 0.2594 0.4593 0.3963 0.4243 0.2015 0.4390 0.4205 0.3951 0.3950 +Reasoning + + + + + BLEU-1 0.2442 0.0758 0.2548 0.1536 0.2480 0.4459 0.3797 0.4108 0.1912 0.4175 0.3969 0.3767 0.3795 + VR EM 0.2011 0.0460 0.1552 0.0977 0.2011 0.3851 0.2989 0.3391 0.1609 0.3276 0.3046 0.3103 0.3103 + LLM-Judge 0.3046 0.1264 0.2931 0.1954 0.3046 0.6149 0.5000 0.5489 0.2586 0.5632 0.5632 0.4598 0.4885 + F1 0.2411 0.1516 0.4056 0.2116 0.2428 0.4367 0.4503 0.4385 0.2101 0.4980 0.4954 0.4343 0.4756 + BLEU-1 0.1739 0.0982 0.3012 0.1497 0.1770 0.3347 0.3476 0.3355 0.1429 0.3915 0.3871 0.3295 0.3667 + MR EM 0.0340 0.0049 0.0631 0.0194 0.0340 0.0631 0.0777 0.0680 0.0194 0.0971 0.0971 0.0631 0.0874 + LLM-Judge 0.2985 0.1238 0.6917 0.2767 0.3058 0.8204 0.7379 0.7961 0.2791 0.8228 0.8204 0.7063 0.7888 + F1 0.2354 0.1805 0.3331 0.2429 0.2354 0.4292 0.3863 0.4686 0.2181 0.4176 0.4062 0.3835 0.3707 + BLEU-1 0.2005 0.1521 0.2798 0.2044 0.2000 0.3741 0.3305 0.4087 0.1883 0.3660 0.3606 0.3346 0.3152 + KR EM 0.1235 0.0988 0.1358 0.0988 0.1235 0.2099 0.1975 0.2222 0.1235 0.2099 0.1975 0.2099 0.1481 +Knowledge Management + + + + + LLM-Judge 0.3395 0.2160 0.5988 0.3210 0.3395 0.7407 0.5988 0.7593 0.2840 0.7469 0.6852 0.6358 0.6173 + F1 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 + BLEU-1 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 + CD EM 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 + LLM-Judge 0.3457 0.3580 0.3333 0.3457 0.3333 0.3580 0.3457 0.3457 0.3580 0.3580 0.3457 0.3457 0.3210 + F1 0.9958 1.0000 0.9581 0.9891 0.9958 0.9849 0.9159 0.9785 0.9946 0.9532 0.9421 0.9749 0.9461 + BLEU-1 0.9953 1.0000 0.9575 0.9891 0.9953 0.9844 0.9141 0.9784 0.9946 0.9524 0.9413 0.9741 0.9459 + AR EM 0.9946 1.0000 0.9565 0.9891 0.9946 0.9837 0.9130 0.9783 0.9946 0.9511 0.9402 0.9728 0.9457 + LLM-Judge 0.9783 0.9837 0.9402 0.9728 0.9783 0.9674 0.9375 0.9620 0.9837 0.9429 0.9375 0.9565 0.9429 + F1 0.3625 0.2558 0.5478 0.3560 0.3619 0.5282 0.6038 0.6021 0.3354 0.6791 0.6688 0.6322 0.6457 + BLEU-1 0.3279 0.2255 0.4946 0.3164 0.3279 0.4792 0.5475 0.5483 0.2999 0.6288 0.6164 0.5817 0.5887 + Overall EM 0.2519 0.1596 0.3711 0.2350 0.2507 0.3402 0.4296 0.4208 0.2279 0.4915 0.4798 0.4711 0.4611 + LLM-Judge 0.4331 0.3115 0.6715 0.4299 0.4307 0.7306 0.7119 0.7463 0.4129 0.7957 0.7823 0.7329 0.7586 + + + + +ity to accurately recognize or verify the dog breed Commons licenses were used. All conversations +shown in the image, these models are unable to and question–answer pairs were reviewed by an- +align the updated textual knowledge with the vi- notators to remove inaccurate, misleading, or in- +sual evidence, leading to incorrect final predictions. appropriate content and to avoid the inclusion of +This case demonstrates that effective knowledge sensitive material. The benchmark construction +resolution requires both explicit mechanisms for process does not involve real user interactions or +memory revision and robust visual understanding private user data. Thus, the dataset does not involve +to ground updated knowledge in perceptual evi- data privacy violations and is intended solely for +dence, which remains largely underexplored in cur- research and benchmarking purposes. +rent multimodal designs. + A.8.2 Annotation +A.8 Ethics Statement + All annotation and verification work in this bench- +A.8.1 Data Privacy mark was conducted exclusively for data quality +To ensure data privacy, the benchmark does not in- control and validation purposes. The annotation +clude any personally identifiable information from tasks were limited to checking the correctness, con- +real user conversations. All conversations were sistency, and completeness of conversational data +either generated by LLMs or reconstructed from and model-generated outputs, including verifying +open-sourced paper repositories, and were subse- dialogue coherence across sessions, validating mul- +quently verified and edited under strong human timodal grounding, and reviewing question–answer +supervision by the author team. The majority of pairs for clarity and correctness. Annotators were +images and portions of single-session dialogue data treated solely as an interface for data verification +were collected from open-source repositories. For rather than as research subjects, and no analysis +newly collected images sourced from the web, only of annotator behavior, agreement patterns, or in- +materials released under permissive or Creative dividual decision strategies was performed. No + Table 12: Benchmark performance results on Qwen-2.5-VL-7B with the retrieval number K=15. The best and +second-performed memory model(s) are highlighted with orange and blue backgrounds. + + Qwen-2.5-VL-7B (K=15) Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2376 0.1511 0.6123 0.2765 0.2391 0.5928 0.6258 0.6382 0.2150 0.6674 0.6626 0.6482 0.6182 + BLEU-1 0.1865 0.1071 0.5273 0.2124 0.1903 0.5098 0.5321 0.5462 0.1626 0.5808 0.5678 0.5698 0.5319 + FR EM 0.0913 0.0502 0.3151 0.0913 0.0913 0.3288 0.3105 0.3516 0.0685 0.3744 0.3379 0.3699 0.3288 + LLM-Judge 0.2626 0.1416 0.8082 0.3379 0.2626 0.8539 0.8037 0.8447 0.2260 0.8767 0.8721 0.8151 0.8174 +Extract. & Adapt. + + + + + F1 0.1992 0.0794 0.7812 0.3425 0.1954 0.6239 0.7905 0.8022 0.1658 0.8725 0.8452 0.8673 0.8607 + BLEU-1 0.1873 0.0680 0.7359 0.3032 0.1840 0.5834 0.7160 0.7337 0.1473 0.8362 0.8162 0.8295 0.8084 + VS EM 0.1601 0.0425 0.5752 0.2320 0.1569 0.4118 0.5915 0.6111 0.1078 0.6536 0.6471 0.6961 0.6569 + LLM-Judge 0.1961 0.0719 0.7696 0.3366 0.1895 0.5964 0.7484 0.7958 0.1683 0.8742 0.8448 0.8676 0.8497 + F1 0.4500 0.3410 0.6410 0.4981 0.4486 0.2924 0.5900 0.5302 0.4147 0.8071 0.7517 0.7782 0.7975 + BLEU-1 0.3799 0.2838 0.5694 0.4283 0.3798 0.2295 0.5096 0.4502 0.3477 0.7307 0.6785 0.7128 0.7291 + TTL EM 0.2374 0.1662 0.4540 0.2908 0.2374 0.1009 0.3769 0.3264 0.2107 0.5964 0.5697 0.5994 0.6172 + LLM-Judge 0.7092 0.6810 0.8279 0.7611 0.7033 0.7092 0.7953 0.7582 0.7107 0.9050 0.8591 0.9050 0.9065 + F1 0.2545 0.1930 0.4864 0.3335 0.2553 0.5661 0.5760 0.5614 0.2294 0.5798 0.5759 0.5752 0.5882 + BLEU-1 0.2363 0.1684 0.4539 0.3122 0.2363 0.5326 0.5485 0.5323 0.2065 0.5482 0.5413 0.5468 0.5602 + TR EM 0.1545 0.0976 0.3252 0.2276 0.1545 0.3496 0.4309 0.3902 0.1382 0.4228 0.4065 0.4472 0.4472 + LLM-Judge 0.2805 0.1870 0.6707 0.3943 0.2764 0.8008 0.7236 0.7358 0.2480 0.7724 0.8008 0.7358 0.7561 + F1 0.2552 0.1155 0.3303 0.2119 0.2594 0.4593 0.4458 0.4676 0.2015 0.4678 0.4913 0.4697 0.3860 +Reasoning + + + + + BLEU-1 0.2442 0.0998 0.3144 0.2005 0.2480 0.4459 0.4335 0.4533 0.1912 0.4474 0.4712 0.4494 0.3713 + VR EM 0.2011 0.0690 0.2299 0.1379 0.2011 0.3851 0.3563 0.3678 0.1609 0.3563 0.3736 0.3621 0.2874 + LLM-Judge 0.3046 0.1494 0.4368 0.2644 0.3046 0.6149 0.5632 0.5862 0.2586 0.5690 0.5833 0.5632 0.4799 + F1 0.2411 0.1747 0.4549 0.2471 0.2428 0.4367 0.4723 0.4547 0.2101 0.5042 0.5004 0.4841 0.4839 + BLEU-1 0.1739 0.1225 0.3372 0.1813 0.1770 0.3347 0.3698 0.3519 0.1429 0.3910 0.3840 0.3760 0.3748 + MR EM 0.0340 0.0146 0.0777 0.0243 0.0340 0.0631 0.0825 0.0825 0.0194 0.0922 0.0971 0.0874 0.0874 + LLM-Judge 0.2985 0.1893 0.8058 0.3811 0.3058 0.8204 0.7913 0.8301 0.2791 0.8568 0.8665 0.8010 0.8252 + F1 0.2354 0.1685 0.3740 0.2752 0.2354 0.4292 0.4470 0.4888 0.2181 0.4701 0.4629 0.4242 0.4175 + BLEU-1 0.2005 0.1290 0.3203 0.2338 0.2000 0.3741 0.3935 0.4345 0.1883 0.4166 0.4052 0.3738 0.3508 + KR EM 0.1235 0.0617 0.1605 0.1235 0.1235 0.2099 0.2222 0.2593 0.1235 0.2469 0.2222 0.2222 0.1975 +Knowledge Management + + + + + LLM-Judge 0.3395 0.2469 0.6296 0.3704 0.3395 0.7407 0.6605 0.7654 0.2840 0.7963 0.7716 0.7284 0.7099 + F1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 + BLEU-1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 + CD EM 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 + LLM-Judge 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3210 0.3580 0.3580 0.3704 0.3704 0.3457 0.3210 + F1 0.9958 0.9958 0.9513 0.9843 0.9958 0.9849 0.9224 0.9842 0.9946 0.9513 0.9523 0.9635 0.9464 + BLEU-1 0.9953 0.9953 0.9512 0.9840 0.9953 0.9844 0.9201 0.9840 0.9946 0.9512 0.9518 0.9629 0.9461 + AR EM 0.9946 0.9946 0.9511 0.9837 0.9946 0.9837 0.9185 0.9837 0.9946 0.9511 0.9511 0.9620 0.9457 + LLM-Judge 0.9783 0.9783 0.9375 0.9674 0.9783 0.9674 0.9429 0.9674 0.9837 0.9511 0.9348 0.9457 0.9375 + F1 0.3625 0.2794 0.6040 0.4035 0.3619 0.5282 0.6168 0.6182 0.3354 0.6925 0.6775 0.6788 0.6667 + BLEU-1 0.3279 0.2489 0.5503 0.3619 0.3279 0.4792 0.5574 0.5599 0.2999 0.6393 0.6244 0.6296 0.6129 + Overall EM 0.2519 0.1894 0.4150 0.2700 0.2507 0.3402 0.4214 0.4290 0.2279 0.4915 0.4804 0.4997 0.4793 + LLM-Judge 0.4331 0.3504 0.7408 0.4906 0.4307 0.7306 0.7458 0.7694 0.4129 0.8209 0.8077 0.7969 0.7873 + + + + +personally identifiable information or demographic ily targets English-language multimodal conversa- +attributes were collected or recorded during the an- tions across diverse daily-life and domain-specific +notation process. The annotation workflow did not scenarios, as illustrated in Appendix A.2. No de- +involve tracking individual annotators, comparing mographic attributes of real individuals are repre- +annotator performance, or modeling human judg- sented or analyzed. The benchmark and dataset +ment patterns. All annotation results were used will be released publicly with clear documentation, +only to improve data quality and benchmark reli- permitting use for research purposes. +ability, with the research focus remaining only on +the properties of the data, models, and evaluation +framework rather than on any human behavior. All A.9 Potential Risks +annotators were fully informed that the annotated +data would be released publicly for research pur- + This benchmark is designed for research and eval- +poses. Since all annotation and verification work + uation purposes only. While it does not involve +was conducted by the author team as part of the re- + real user data or personal information, potential +search process, no external recruitment or monetary + risks may arise from unintended misuse, such as +compensation was involved. The benchmark study + over-interpreting benchmark results as indicators +does not involve any privacy data of real users or + of real-world deployment readiness. In addition, +human-subject research. + models evaluated on this benchmark may inherit + biases present in underlying pretrained language +A.8.3 Scientific Artifacts + model backbones (Schramowski et al., 2022). We +All data sources and models underlying the bench- mitigate these risks by clearly scoping the bench- +mark are used with explicit references and official mark to controlled memory research settings and +links. Detailed information can be found in Ap- by providing transparent documentation of data +pendix A.2.2, A.4, and A.5. The benchmark primar- sources, evaluation protocols, and limitations. + Table 13: Benchmark performance results on Qwen-2.5-VL-7B with the retrieval number K=20. The best and +second-performed memory model(s) are highlighted with orange and blue backgrounds. + + Qwen-2.5-VL-7B (K=20) Full (Text) FIFO NaiveRAG Gen. Agent Reflexion MemGPT A-Mem MemoryOS Full (MM) MuRAG UniversalRAG NGM AUGUSTUS + F1 0.2376 0.1743 0.6303 0.3020 0.2391 0.5928 0.6145 0.6265 0.2150 0.6596 0.6609 0.6589 0.6216 + BLEU-1 0.1865 0.1295 0.5438 0.2366 0.1903 0.5098 0.5210 0.5396 0.1626 0.5656 0.5639 0.5813 0.5335 + FR EM 0.0913 0.0548 0.3242 0.1142 0.0913 0.3288 0.2922 0.3379 0.0685 0.3516 0.3425 0.3790 0.3379 + LLM-Judge 0.2626 0.1598 0.8174 0.3744 0.2626 0.8539 0.8174 0.8288 0.2260 0.8950 0.8790 0.8402 0.8128 +Extract. & Adapt. + + + + + F1 0.1992 0.0953 0.8144 0.3567 0.1954 0.6239 0.7782 0.7963 0.1658 0.8676 0.8444 0.8583 0.8647 + BLEU-1 0.1873 0.0849 0.7704 0.3187 0.1840 0.5834 0.7026 0.7300 0.1473 0.8341 0.8160 0.8198 0.8230 + VS EM 0.1601 0.0588 0.5980 0.2386 0.1569 0.4118 0.5882 0.6078 0.1078 0.6503 0.6503 0.6765 0.6569 + LLM-Judge 0.1961 0.0850 0.8023 0.3546 0.1895 0.5964 0.7337 0.7925 0.1683 0.8856 0.8611 0.8660 0.8611 + F1 0.4500 0.3789 0.6104 0.4900 0.4486 0.2924 0.5542 0.5353 0.4147 0.7841 0.7211 0.7911 0.7859 + BLEU-1 0.3799 0.3180 0.5355 0.4137 0.3798 0.2295 0.4735 0.4566 0.3477 0.7122 0.6478 0.7250 0.7184 + TTL EM 0.2374 0.1899 0.4243 0.2849 0.2374 0.1009 0.3561 0.3442 0.2107 0.5905 0.5312 0.6142 0.6172 + LLM-Judge 0.7092 0.6929 0.8131 0.7448 0.7033 0.7092 0.7611 0.7760 0.7107 0.8961 0.8694 0.8976 0.9021 + F1 0.2545 0.1858 0.5154 0.3484 0.2553 0.5661 0.5502 0.5941 0.2294 0.5642 0.5613 0.5445 0.5890 + BLEU-1 0.2363 0.1652 0.4848 0.3255 0.2363 0.5326 0.5220 0.5679 0.2065 0.5345 0.5291 0.5180 0.5615 + TR EM 0.1545 0.0976 0.3577 0.2358 0.1545 0.3496 0.3821 0.4472 0.1382 0.3984 0.3984 0.4146 0.4390 + LLM-Judge 0.2805 0.1870 0.7073 0.4268 0.2764 0.8008 0.7073 0.7398 0.2480 0.7967 0.7846 0.7073 0.7642 + F1 0.2552 0.1383 0.3654 0.2171 0.2594 0.4593 0.4216 0.4410 0.2015 0.5320 0.4344 0.4772 0.3695 +Reasoning + + + + + BLEU-1 0.2442 0.1208 0.3507 0.2016 0.2480 0.4459 0.4096 0.4304 0.1912 0.5130 0.4192 0.4563 0.3550 + VR EM 0.2011 0.0862 0.2644 0.1264 0.2011 0.3851 0.3391 0.3621 0.1609 0.4138 0.3333 0.3678 0.2644 + LLM-Judge 0.3046 0.1954 0.4770 0.2672 0.3046 0.6149 0.5517 0.5891 0.2586 0.6322 0.5460 0.5718 0.4483 + F1 0.2411 0.1837 0.4678 0.2616 0.2428 0.4367 0.4732 0.4554 0.2101 0.4988 0.4955 0.4802 0.4742 + BLEU-1 0.1739 0.1271 0.3523 0.1932 0.1770 0.3347 0.3656 0.3550 0.1429 0.3863 0.3757 0.3664 0.3683 + MR EM 0.0340 0.0194 0.0922 0.0291 0.0340 0.0631 0.0874 0.0777 0.0194 0.0874 0.0874 0.0825 0.0971 + LLM-Judge 0.2985 0.2039 0.8107 0.4029 0.3058 0.8204 0.7913 0.8374 0.2791 0.8786 0.8689 0.8107 0.8155 + F1 0.2354 0.1924 0.4200 0.2909 0.2354 0.4292 0.4290 0.4597 0.2181 0.4648 0.4604 0.4354 0.3967 + BLEU-1 0.2005 0.1546 0.3666 0.2530 0.2000 0.3741 0.3657 0.4083 0.1883 0.4060 0.3969 0.3858 0.3343 + KR EM 0.1235 0.0741 0.2099 0.1481 0.1235 0.2099 0.1852 0.2222 0.1235 0.2346 0.1975 0.2593 0.1728 +Knowledge Management + + + + + LLM-Judge 0.3395 0.2593 0.7037 0.3951 0.3395 0.7407 0.7099 0.7531 0.2840 0.8025 0.7778 0.7593 0.6975 + F1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 + BLEU-1 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 + CD EM 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 + LLM-Judge 0.3457 0.3580 0.3457 0.3086 0.3333 0.3580 0.3333 0.3580 0.3580 0.3704 0.3457 0.3580 0.3333 + F1 0.9958 0.9956 0.9514 0.9840 0.9958 0.9849 0.9233 0.9677 0.9946 0.9307 0.9322 0.9471 0.9519 + BLEU-1 0.9953 0.9952 0.9512 0.9838 0.9953 0.9844 0.9210 0.9676 0.9946 0.9297 0.9306 0.9466 0.9515 + AR EM 0.9946 0.9946 0.9511 0.9837 0.9946 0.9837 0.9185 0.9674 0.9946 0.9293 0.9293 0.9457 0.9511 + LLM-Judge 0.9783 0.9783 0.9348 0.9674 0.9783 0.9674 0.9457 0.9565 0.9837 0.9266 0.9239 0.9348 0.9429 + F1 0.3625 0.2966 0.6157 0.4118 0.3619 0.5282 0.6018 0.6132 0.3354 0.6884 0.6602 0.6785 0.6629 + BLEU-1 0.3279 0.2651 0.5618 0.3683 0.3279 0.4792 0.5409 0.5572 0.2999 0.6357 0.6069 0.6286 0.6117 + Overall EM 0.2519 0.2005 0.4243 0.2741 0.2507 0.3402 0.4085 0.4296 0.2279 0.4874 0.4635 0.4985 0.4787 + LLM-Judge 0.4331 0.3644 0.7554 0.5018 0.4307 0.7306 0.7390 0.7700 0.4129 0.8320 0.8068 0.7992 0.7846 + + + + +A.10 Use of AI Assistants +LLMs are used in this work strictly as auxiliary +tools for data construction, an evaluation metric, +and limited language polishing. During dataset +construction, LLMs assist in drafting candidate +multi-session conversational texts under predefined +structural constraints and scenario specifications. +These drafts are subsequently reviewed, edited, and +refined by annotators, who also insert appropriate +images and ensure that multimodal dependencies +are genuine, necessary, and non-trivial for down- +stream tasks. LLMs are further used to generate +part of the candidate question–answer pairs and +to perform preliminary checks for coherence, clar- +ity, and factual consistency. All QA pairs are then +manually verified and revised by annotators. In +addition, LLMs are used in a limited manner for +language polishing of the manuscript, including +improving fluency and presentation. In our eval- +uation, we also adopt LLM-as-a-Judge as one of +the evaluation metrics, following common practice +in prior work (Li et al., 2025). All technical con- +tent, experimental analysis, and scientific claims +are authored and finalized by the authors. + Table 14: Retrieval evaluation results on Qwen-2.5-VL-7B with different retrieval number K. Since answer refusal +(AR) questions intentionally introduce incorrect information and contain no supporting evidence in the conversation, +evaluation metrics for this category are not supported. + + Recall@K Precision@K Hit@K + Qwen-2.5-VL-7B 5 10 15 20 5 10 15 20 5 10 15 20 + FIFO 0.0116 0.0381 0.0685 0.0950 0.0043 0.0087 0.0123 0.0129 0.0196 0.0491 0.0792 0.1081 + Gen. Agent 0.1707 0.2385 0.2854 0.3153 0.0955 0.0684 0.0544 0.0453 0.2718 0.3491 0.3962 0.4257 + NaiveRAG 0.5381 0.6723 0.7420 0.7877 0.2694 0.1757 0.1315 0.1047 0.7236 0.8179 0.8605 0.8900 + Overall MuRAG 0.7506 0.8601 0.8990 0.9228 0.3686 0.2220 0.1572 0.1220 0.8861 0.9384 0.9548 0.9666 + UniversalRAG 0.7311 0.8411 0.8781 0.8998 0.3691 0.2206 0.1555 0.1204 0.8697 0.9227 0.9404 0.9496 + NGM 0.6192 0.7475 0.7892 0.8065 0.3564 0.3457 0.3450 0.3424 0.7708 0.8612 0.8874 0.9037 + AUGUSTUS 0.6729 0.7529 0.7785 0.7860 0.3341 0.2488 0.2213 0.2124 0.8553 0.8893 0.8959 0.8978 + FIFO 0.0006 0.0354 0.0514 0.0793 0.0009 0.0059 0.0064 0.0075 0.0046 0.0365 0.0548 0.0822 + Gen. Agent 0.1027 0.1363 0.1708 0.2062 0.0320 0.0237 0.0225 0.0215 0.1370 0.1872 0.2420 0.2922 + NaiveRAG 0.4802 0.6297 0.7379 0.7811 0.2082 0.1511 0.1239 0.1014 0.6849 0.8265 0.8813 0.9041 + FR MuRAG 0.6248 0.7712 0.8228 0.8647 0.2630 0.1840 0.1397 0.1146 0.7991 0.9041 0.9178 0.9406 + UniversalRAG 0.6387 0.7940 0.8328 0.8768 0.2676 0.1913 0.1431 0.1180 0.8265 0.9178 0.9269 0.9498 + NGM 0.5025 0.6557 0.6999 0.7102 0.2348 0.2311 0.2329 0.2289 0.6712 0.8037 0.8265 0.8447 + AUGUSTUS 0.5766 0.6626 0.6771 0.6862 0.2428 0.2025 0.1779 0.1748 0.7717 0.8174 0.8219 0.8402 + FIFO 0.0172 0.0485 0.0678 0.0842 0.0059 0.0082 0.0085 0.0074 0.0261 0.0556 0.0719 0.0882 + Gen. Agent 0.2173 0.2801 0.3309 0.3505 0.0706 0.0467 0.0379 0.0312 0.2843 0.3660 0.4183 0.4444 + NaiveRAG 0.6723 0.7979 0.8300 0.8724 0.2366 0.1474 0.1037 0.0815 0.7712 0.8693 0.8856 0.9216 + VS MuRAG 0.9080 0.9534 0.9660 0.9774 0.3379 0.1827 0.1237 0.0935 0.9444 0.9673 0.9739 0.9869 + UniversalRAG 0.8906 0.9438 0.9570 0.9651 0.3301 0.1801 0.1218 0.0918 0.9444 0.9739 0.9837 0.9902 + NGM 0.7889 0.8799 0.8982 0.9217 0.3445 0.3204 0.3134 0.3058 0.8562 0.9183 0.9346 0.9542 + AUGUSTUS 0.8363 0.8872 0.9056 0.9081 0.3124 0.2284 0.2062 0.1960 0.9248 0.9510 0.9542 0.9510 + FIFO 0.0026 0.0263 0.0938 0.1358 0.0042 0.0128 0.0265 0.0288 0.0208 0.0504 0.1246 0.1721 + Gen. Agent 0.3152 0.4425 0.5036 0.5488 0.2570 0.1831 0.1375 0.1119 0.5460 0.6528 0.7003 0.7240 + NaiveRAG 0.6837 0.8412 0.9017 0.9234 0.5501 0.3436 0.2457 0.1884 0.9496 0.9703 0.9881 0.9911 + TTL MuRAG 0.8523 0.9566 0.9760 0.9785 0.6754 0.3932 0.2671 0.2007 0.9822 0.9852 0.9941 0.9970 + UniversalRAG 0.8617 0.9583 0.9682 0.9787 0.6967 0.3970 0.2669 0.2018 0.9763 0.9822 0.9881 0.9941 + NGM 0.7617 0.8919 0.9194 0.9203 0.7029 0.6919 0.6934 0.6930 0.9407 0.9763 0.9822 0.9822 + AUGUSTUS 0.7231 0.8508 0.8903 0.9088 0.5569 0.3690 0.3094 0.2914 0.9733 0.9822 0.9881 0.9911 + FIFO 0.0081 0.0122 0.0325 0.0569 0.0033 0.0024 0.0033 0.0037 0.0081 0.0163 0.0325 0.0569 + Gen. Agent 0.0447 0.1043 0.1301 0.1558 0.0130 0.0154 0.0130 0.0122 0.0650 0.1463 0.1707 0.2033 + NaiveRAG 0.3828 0.5000 0.5413 0.6009 0.1301 0.0870 0.0640 0.0524 0.5610 0.6585 0.6829 0.7317 + TR MuRAG 0.6640 0.7724 0.8266 0.8557 0.2163 0.1285 0.0927 0.0732 0.8455 0.8943 0.9187 0.9187 + UniversalRAG 0.6938 0.8089 0.8652 0.8733 0.2293 0.1341 0.0970 0.0736 0.8618 0.9106 0.9350 0.9431 + NGM 0.4898 0.5928 0.6179 0.6321 0.1846 0.1688 0.1626 0.1609 0.6504 0.7561 0.7805 0.8049 + AUGUSTUS 0.6497 0.7243 0.7595 0.7683 0.2280 0.1743 0.1601 0.1509 0.8455 0.8780 0.8780 0.8780 + FIFO 0.0000 0.0235 0.0551 0.0893 0.0000 0.0075 0.0126 0.0155 0.0000 0.0287 0.0632 0.1092 + Gen. Agent 0.1439 0.1888 0.2521 0.2817 0.0885 0.0621 0.0544 0.0448 0.2874 0.3391 0.4138 0.4310 + NaiveRAG 0.3093 0.4774 0.6010 0.6632 0.1540 0.1264 0.1115 0.0948 0.5057 0.6782 0.7759 0.8333 + VR MuRAG 0.6158 0.7774 0.8551 0.8916 0.3299 0.2236 0.1701 0.1353 0.8276 0.9080 0.9483 0.9598 + UniversalRAG 0.5497 0.6978 0.7560 0.7979 0.3356 0.2149 0.1586 0.1259 0.7759 0.8333 0.8621 0.8851 + NGM 0.4803 0.6351 0.7183 0.7541 0.2603 0.2707 0.2761 0.2762 0.6839 0.7989 0.8506 0.8793 + AUGUSTUS 0.5110 0.5722 0.5796 0.5924 0.3430 0.3139 0.3032 0.3045 0.7644 0.8103 0.8103 0.8218 + FIFO 0.0178 0.0591 0.0736 0.0914 0.0058 0.0117 0.0097 0.0085 0.0243 0.0728 0.0777 0.0971 + Gen. Agent 0.1219 0.1752 0.2122 0.2376 0.0505 0.0359 0.0298 0.0252 0.2039 0.2621 0.2913 0.3204 + NaiveRAG 0.5659 0.6600 0.7306 0.7793 0.2010 0.1257 0.0958 0.0784 0.7379 0.8252 0.8738 0.8981 + MR MuRAG 0.7626 0.8527 0.9034 0.9237 0.2641 0.1597 0.1175 0.0925 0.9175 0.9612 0.9806 0.9806 + UniversalRAG 0.7506 0.8590 0.9108 0.9364 0.2612 0.1631 0.1204 0.0949 0.8981 0.9563 0.9806 0.9854 + NGM 0.6023 0.7464 0.7927 0.8133 0.2551 0.2415 0.2401 0.2394 0.7573 0.8835 0.9223 0.9369 + AUGUSTUS 0.7162 0.7748 0.8048 0.8014 0.2596 0.1964 0.1783 0.1684 0.8786 0.9175 0.9320 0.9223 + FIFO 0.0412 0.0607 0.0885 0.1091 0.0099 0.0086 0.0099 0.0099 0.0494 0.0864 0.1111 0.1235 + Gen. Agent 0.0782 0.1091 0.1214 0.1389 0.0247 0.0185 0.0148 0.0136 0.1111 0.1728 0.1852 0.2346 + NaiveRAG 0.3899 0.5300 0.5808 0.6529 0.1605 0.1111 0.0823 0.0685 0.5679 0.7160 0.7531 0.8025 + KR MuRAG 0.5752 0.7495 0.8015 0.8424 0.2272 0.1506 0.1119 0.0895 0.7531 0.8889 0.9012 0.9259 + UniversalRAG 0.4750 0.6453 0.7433 0.7841 0.1852 0.1259 0.1029 0.0827 0.6790 0.8272 0.8889 0.8889 + NGM 0.4141 0.5599 0.6626 0.6863 0.1934 0.1841 0.1914 0.1907 0.6049 0.7037 0.7778 0.8025 + AUGUSTUS 0.4483 0.5523 0.5860 0.5970 0.1895 0.1480 0.1324 0.1287 0.6420 0.7160 0.7284 0.7284 + FIFO 0.0432 0.0494 0.0617 0.0741 0.0099 0.0062 0.0049 0.0043 0.0494 0.0494 0.0617 0.0741 + Gen. Agent 0.0432 0.1091 0.1728 0.1934 0.0148 0.0198 0.0230 0.0204 0.0617 0.1852 0.2469 0.2593 + NaiveRAG 0.3868 0.4650 0.5539 0.6284 0.1333 0.0827 0.0667 0.0574 0.5432 0.5926 0.7037 0.7407 + CD MuRAG 0.6383 0.7868 0.8218 0.8881 0.2148 0.1296 0.0897 0.0759 0.7407 0.8519 0.8765 0.9259 + UniversalRAG 0.4877 0.6008 0.6605 0.6687 0.1704 0.1037 0.0765 0.0586 0.5926 0.7160 0.7407 0.7284 + NGM 0.4428 0.5621 0.6074 0.6383 0.1774 0.1673 0.1642 0.1596 0.5802 0.7160 0.7407 0.7654 + AUGUSTUS 0.6049 0.6584 0.6893 0.6770 0.2123 0.1578 0.1446 0.1385 0.6914 0.7531 0.7778 0.7654 + Relevant clue snippets in the conversation history + + Great! Let me start with the Cap-Sleeved Midi Dresses. Here’s the first one. + + +Lin + Oh wow, the moment I opened this one, the shine caught my eye immediately. Let me + take in the details. + Assistant + + What do you think of this dress? + + +Lin This dress has such a striking metallic sheen—like it was made for evening lights. The + bronze tones and scalloped stripes feel glamorous but still structured. It’s definitely a + confidence-boosting piece. + Assistant + + + Here's the second dress. + + +Lin + Aww, this one immediately gives off a softer energy. The floral pattern feels calming in + contrast to the first one. + Assistant + More conversation on Cap-Sleeved Midi Dresses with more visual examples… + + + + + According to the conversation, what category is the clothing in the picture? + + + + Insufficient visual Insufficient visual + Insufficient visual + & adaptation ability. & adaptation ability. + & adaptation ability. + The clothing in the picture is + Midi Dress. dress. + a fitted, knee-length dress. + + + NaiveRAG A-Mem MemoryOS + + + Insufficient + adaptation ability. + + + Cap-Sleeved Midi Dresses. dress. Cap-Sleeved Midi Dress. + + + MuRAG UniversalRAG AUGUSTUS + + + + Figure 16: Case study with the test-time learning example. + Relevant clue snippets in the conversation history + + Not yet! I want something that fits her personality — gentle but lively. I’ve been asking + my friends for ideas too. My friend Amy has a Cairn Terrier, and she gave me some tips. + +Lena + Oh, Cairn Terriers! They’re quite different from Maltese dogs: more energetic and + adventurous. Amy’s must keep her busy! What did she say about naming pets? + Assistant + + Amy said her dog is super active and always digging around in the yard. Apparently, that’s + part of their nature. They were bred to hunt small animals in rocky areas! + + +Lena Exactly! Cairn Terriers are brave and curious little dogs. They love to explore and have + tons of energy. They can be stubborn at times, but that’s just because they’re so + independent. Did Amy mention if it’s hard to train her dog? + … Assistant + Othe dialogs about Lucy’s and Lena’s classmate’s dog… + + + + Is the dog in the picture the same breed as the pet of any friend or classmate mentioned by Lena? + Answer Yes or No. If yes, then please specify which person's pet it shares the same breed with. + + + Insufficient Insufficient Insufficient + retrieval. retrieval. retrieval. + No. No. No. + + NaiveRAG A-Mem MemoryOS + + + Correct retrieved clues, + but incorrect reasoning. Insufficient + retrieval. + Yes, the dog in the No. The dog in the picture is a + Cairn Terrier, while Lena's + No. + picture is the same + breed as Amy’s. friend Amy has a Cairn Terrier. + The dog in the picture is not AUGUSTUS + the same breed as Amy's pet. + MuRAG + + UniversalRAG + + + Figure 17: Case study with the visual-centric reasoning example. + Relevant clue snippets in the conversation history + + My sister actually loves Standard Poodles. She says their elegance and athletic build + make them her dream dog. + +Lena + That totally makes sense! People who appreciate both intelligence and style often adore + Standard Poodles. They’re such graceful companions and do so well in active families. + Assistant + + I think I’d love spending time with a Miniature Poodle. They seem like the perfect + balance: smart, active, but still small enough for apartment life. + + You’re absolutely right, Lena. Miniature Poodles often fit well into both active and cozy +Lena + lifestyles. They enjoy long walks and puzzles but also love curling up beside their owners. + Given your interest in animal behavior, you’d probably find their problem-solving + instincts fascinating. + Assistant + Other dialogs… + + Oh wait, I just realized I got it wrong earlier. My sister doesn’t actually like Standard + Poodles. Her favorite breed is the Standard Schnauzer, as shown in the image! + +Lena Ah, that explains it! Standard Schnauzers have a totally different charm — confident, + spirited, and with those adorable beards. They’re smart and loyal too, just in their own + unique way. + … Assistant + + + + Is the dog in the picture the breed that Lena's sister likes? + + + Insufficient Insufficient + visual ability. visual ability. + Insufficient + No, the dog in the picture is visual ability. No, the dog in the picture is not the + not the breed that Lena's breed that Lena's sister likes. + sister likes. Lena's sister Lena's sister prefers Standard + prefers Standard Schnauzers. No. Schnauzers over Standard Poodles. + + + NaiveRAG A-Mem MemoryOS + + Lack knowledge Lack knowledge Lack knowledge + update ability. update ability. update ability. + No, the dog in the picture is not + the breed that Lena's sister No. No. + likes. Lena's sister actually loves + Standard Poodles. + UniversalRAG AUGUSTUS + MuRAG + + + + Figure 18: Case study with the knowledge resolution example. + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/probe_meta.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/probe_meta.py new file mode 100644 index 0000000..6267ac8 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/probe_meta.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 +"""probe_meta.py -- pull the citation_* meta tags out of raw/abs.html.""" +import html +import os +import re + +BASE = os.path.dirname(os.path.abspath(__file__)) +t = open(os.path.join(BASE, "raw", "abs.html"), encoding="utf-8", errors="replace").read() + + +def meta(name): + m = re.search(r'(.*?)', t, re.S) +if m: + a = re.sub(r"<[^>]+>", "", m.group(1)) + print("---ABSTRACT---") + print(html.unescape(a).strip()) + +m = re.search(r'(.*?)', t, re.S) +print("---COMMENTS---") +print(html.unescape(re.sub(r"<[^>]+>", "", m.group(1))).strip() if m else None) + +m = re.search(r'
(.*?)
', t, re.S) +print("---DATELINE---") +print(html.unescape(re.sub(r"<[^>]+>", " ", m.group(1))).strip() if m else None) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/probe_tools.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/probe_tools.py new file mode 100644 index 0000000..548d9b6 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/probe_tools.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +"""probe_tools.py -- report which PDF readers are importable / installed.""" +import shutil +import sys + +for mod in ("pypdf", "PyPDF2", "fitz", "pdfminer", "pdfplumber"): + try: + __import__(mod) + print("%-12s import: OK" % mod) + except Exception as exc: # noqa: BLE001 + print("%-12s import: NO (%s)" % (mod, type(exc).__name__)) + +for exe in ("pdftotext", "pdfimages", "mutool", "qpdf", "gs"): + print("%-12s exe: %s" % (exe, "yes" if shutil.which(exe) else "no")) +print("python:", sys.version.split()[0]) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/quotes.json b/wizard-work/sphinx/archive-memgallery-2026-10-21/quotes.json new file mode 100644 index 0000000..6f52ff1 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/quotes.json @@ -0,0 +1,165 @@ +{ + "paper": { + "arxiv_id": "2601.03515", + "version": "v1", + "title": "Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents", + "authors": [ + "Yuanchen Bei", + "Tianxin Wei", + "Xuying Ning", + "Yanjun Zhao", + "Zhining Liu", + "Xiao Lin", + "Yada Zhu", + "Hendrik Hamann", + "Jingrui He", + "Hanghang Tong" + ], + "submitted": "2026-01-07", + "comments": "34 pages, 18 figures", + "url": "https://arxiv.org/abs/2601.03515" + }, + "hash_method": "quote_sha256 = sha256 of the quote string encoded utf-8; the quote is the exact .rstrip()'d line contents joined with '\\n', with no re-wrapping, no hyphen-dehyphenation and no whitespace normalisation", + "records": [ + { + "id": "Q01", + "section": "Abstract", + "kind": "claim", + "quote": "Long-term memory is a critical capability for\nmultimodal large language model (MLLM)\nagents, particularly in conversational settings\nwhere information accumulates and evolves\nover time. However, existing benchmarks ei-\nther evaluate multi-session memory in text-\nonly conversations or assess multimodal un-\nderstanding within localized contexts, failing\nto evaluate how multimodal memory is pre-\nserved, organized, and evolved across long-\nterm conversational trajectories. Thus, we intro-\nduce Mem-Gallery1\n, a new benchmark for eval-\nuating multimodal long-term conversational\nmemory in MLLM agents. Mem-Gallery fea-", + "locator": "paper.raw.txt lines 20-34", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "3871b3dd377a6b7a5f44e1f27ee13ce728bef4dce82d2bea7e4022928879c3ae" + }, + { + "id": "Q02", + "section": "Abstract", + "kind": "claim", + "quote": "memory in MLLM agents. Mem-Gallery fea-\ntures high-quality multi-session conversations\ngrounded in both visual and textual informa-\ntion, with long interaction horizons and rich\nmultimodal dependencies. Building on this\ndataset, we propose a systematic evaluation", + "locator": "paper.raw.txt lines 34-40", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "4a578f5ee05bccffb16e6ab79849ebb12e0057167bfd1d71c25210045e4c2067" + }, + { + "id": "Q03", + "section": "3.1 Benchmark Construction", + "kind": "statistic", + "quote": "Table 2: Statistics of the Mem-Gallery dataset. | Mem-Gallery | Aspect | Statistics | Conversation Data | Sessions | 240 | Dialogue Rounds | 3,962 | Included Images | 1,003 | Evaluation Data | QA Pairs with Annotated Clues | 1,711 | Included Images | 487", + "locator": "paper.txt lines 359-368 (Table 2)", + "source_file": "paper.txt", + "source_sha256": "1f1d233198f4e15afb807e82433a6148b0cae6edad6c18f78d907bfdc1b08b9a", + "quote_sha256": "090756dcbd28de2a4892dea46c28a6ea40804f4452174f028c64f4093502282c" + }, + { + "id": "Q04", + "section": "3.3 Evaluation Framework", + "kind": "definition", + "quote": "As shown in Figure 4, we design three task fam-\nilies to systematically evaluate multimodal long-\nterm memory as an evolving capability in agentic\nsystems over extended conversations. Specifically,\nmemory extraction and adaptation assess whether\nagents can retrieve relevant multimodal informa-\ntion and adapt it as memory accumulates. Memory\nreasoning examines whether agents can integrate\nand reason over memory, accounting for temporal\ndependencies, visual evidence, and multiple en-\ntities. Memory knowledge management focuses\non how agents regulate long-term memory under\ndynamic states, like handling inconsistencies, re-\nsolving conflicts, and refusing to answer when in-\nformation is outdated, contradictory, or incomplete.", + "locator": "paper.raw.txt lines 664-678", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "71c6c5817b4298910894fedd36cd3e54736e3efdda6ab7dcbddf7d75c7072491" + }, + { + "id": "Q05", + "section": "3.3.1 Memory Extraction & Adaptation", + "kind": "definition", + "quote": "properties, we design three subtask categories. (1)\nFactual Retrieval: evaluate the ability to accurately\nrecall factual details (e.g., user preferences and past\nevents) from multimodal interaction histories. (2)\nVisual-centric Search: evaluate whether the model\ncan identify or retrieve specific visual instances\nfrom memory (e.g., shared images). (3) Test-Time\nLearning: measure the ability to adapt its memory\nto unseen multimodal examples at inference time.", + "locator": "paper.raw.txt lines 684-692", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "db2a449122fee6ea0c73650e4aa4e79d00fbeb10d16e18b8ddb09a214e39d2e0" + }, + { + "id": "Q06", + "section": "4.2 Main Results (RQ1)", + "kind": "finding", + "quote": "RQ1: Explicit multimodal memory preserva-\ntion is critical, but increased architectural com-\nplexity does not necessarily bring better perfor-\nmance. Although with high-quality image cap-\ntions, textual memory baselines still generally ex-\nhibit a performance gap compared to multimodal\napproaches, particularly in memory extraction and\nadaptation. MuRAG, a simple multimodal method,\ncan achieve the best overall performance among", + "locator": "paper.raw.txt lines 781-789", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "a5bb5d295dfb0c8ade7c50ff43944f4c384ce567531596edf6e334704165e9a2" + }, + { + "id": "Q07", + "section": "4.2 Main Results (RQ1)", + "kind": "finding", + "quote": "well-designed textual memory models. Specifically,\nit achieves 11.85%, 7.69%, 12.29%, and 29.06% F1\nimprovement over the best-performed textual mem-\nory on overall, FR, VS, and TTL tasks, respectively.\nFurthermore, MuRAG and UniversalRAG, which\nsimply preserve multimodal information without\nstructured memory organizations, achieve stronger\nperformance than complex multimodal memory\nsystems. For example, MuRAG outperforms 4.11%\nand 5.39% overall F1 improvements over NGM and", + "locator": "paper.raw.txt lines 850-859", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "b4ab03d7b98074fcc1ca3b204c534d8b6bac236876cfc0418b6387db01d1d0cc" + }, + { + "id": "Q08", + "section": "4.2 Main Results (RQ2)", + "kind": "finding", + "quote": "RQ2: Visual information faces harsher to-\nken consumption, making principled organi-\nzation and maintenance critical. Without the\nproper organization, inputting all multimodal infor-\nmation into the context leads Full Memory (MM)\nto perform even worse than Full Memory (Text)\nin both memory extraction and reasoning tasks.\nOverall, Full Memory (MM) performs 8.08% and\n51.85% worse F1 score than Full Memory (Text)\nand MuRAG. As visual information is far more\ntoken-heavy than text (Zhang et al., 2025a), under\nthe token limit, na\u00efvely accumulating multimodal\ncontent can introduce irrelevant visual noise, crowd\nout informative text, and thus hurt performance.", + "locator": "paper.raw.txt lines 865-878", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "0c12cb1ebdf3eab169b3b46c485ee2d1fdf029a8d36d76625b2de2916d0b708b" + }, + { + "id": "Q09", + "section": "4.2 Main Results (RQ3-1)", + "kind": "finding", + "quote": "RQ3-1: Existing multimodal memory meth-\nods struggle on memory reasoning tasks. Al-\nthough multimodal memory methods overall out-\nperform textual memory approaches, we observe\nthat their advantages are not pronounced in scenar-\nios requiring reasoning. This is particularly evident\nin multi-entity reasoning and temporal reasoning\ntasks. Notably, even in the visual-centric reasoning\ntask that explicitly relies on visual clues, MemGPT,\na textual memory method, can achieve near-optimal\nperformance. This suggests that existing multi-", + "locator": "paper.raw.txt lines 887-897", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "5ec2efba08a569c8f0fc0e09c84617d4d84237cfa46b370e5bb480f3e93ee069" + }, + { + "id": "Q10", + "section": "4.2 Main Results (RQ3-2)", + "kind": "finding", + "quote": "results, we derive two key observations. First, there\nis a trade-off in refusal behavior. Methods with\nweaker memory, e.g., FIFO, tend to exhibit stronger\nrefusal performance, as they default to refusing\nwhen relevant information cannot be retrieved. In\ncontrast, methods with stronger capabilities on\nmemory extraction and reasoning show poorer re-\nfusal performance, like A-Mem and MuRAG, with\n7.22% and 5.82% worse F1 score than FIFO. This\nsuggests that future methods need to strike a bal-\nance between retrieving relevant information and\ndistinguishing outdated and conflicting informa-\ntion to build safe and hallucination-free memory\nsystems. Second, for both knowledge resolution\nand conflict detection tasks, neither multimodal nor\ntextual memory methods achieve satisfactory per-\nformance. This indicates that designing memory\nsystems to support dynamic conflict detection and\nmemory correction remains an important direction.", + "locator": "paper.raw.txt lines 948-966", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "7745b7806bfe6e2700b470b7ec072cc6e8ba82018df025b20dc5a4c893fd613f" + }, + { + "id": "Q11", + "section": "4.3 Efficiency Analysis (RQ4)", + "kind": "finding", + "quote": "4.3 Efficiency Analysis (RQ4)\nFigure 5 presents an efficiency comparison of exist-\ning memory methods. From the results, we can see\nthat multimodal memory incurs higher overhead\nthan text-only memory in general. Even a rela-\ntively simple multimodal memory like MuRAG can\napproach the computational cost of highly sophis-\nticated textual memory systems, such as A-Mem\nTable 4: Retrieval metrics on different size K.", + "locator": "paper.raw.txt lines 969-978", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "7ea344b36709458e44740233ea9597b0d5e2b0be5be922f51a06c8069d147dd9" + }, + { + "id": "Q12", + "section": "4.4 Retriever Analysis (RQ5)", + "kind": "finding", + "quote": "Table 4 shows multimodal retrievers like MuRAG\nand UniversalRAG achieve substantially higher\nclue recall. Though recall improves as K increases,\nthe expanded retrieval coverage does not con-\nsistently translate into better QA performance.\nFigure 15 shows multimodal memory models typ-\nically exhibit diminishing or saturated task gains\nbeyond a moderate K, and may degrade at larger\nK. Table 4 explains this discrepancy. While the\nRecall of MuRAG and UniversalRAG increases\nas K grows, its Precision drops sharply, indicat-\ning the introduction of substantial noise. NGM\nand AUGUSTUS exhibit more conservative recall\ngrowth with relatively stable Precision, suggesting\nstructured organization or relevance filtering is an\neffective way to balance coverage and noise. Full", + "locator": "paper.raw.txt lines 1001-1017", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "7c29f633e8fea65ab2259721d696659ef7a737adaaf138dfa1132579a666024d" + }, + { + "id": "Q13", + "section": "5 Conclusion", + "kind": "finding", + "quote": "In this paper, we present Mem-Gallery, a bench-\nmark for evaluating multimodal long-term conver-\nsational memory. By grounding multi-session con-\nversations in tightly coupled visual and textual con-\ntexts, Mem-Gallery enables systematic assessment\nbeyond prior text-centric memory or localized mul-\ntimodal context understanding benchmarks. Exten-\nsive evaluation reveals several takeaways. These\nfindings highlight the need for principled memory\norganization, selective retrieval, and robust mainte-", + "locator": "paper.raw.txt lines 1019-1029", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "a2dff4adb75469fcbad472f8088858dd04d806c06e241153b95f76203b23622a" + }, + { + "id": "Q14", + "section": "Limitations", + "kind": "limitation", + "quote": "While Mem-Gallery provides a comprehensive\nevaluation of multimodal long-term conversational\nmemory, the following limitations remain. First,\nthe benchmark focuses on vision\u2013language con-\nversational settings and does not explicitly cover\nother modalities such as audio or embodied signals,\nwhich may be relevant in broader agentic scenar-\nios. Second, the evaluation primarily focuses on\nmemory-centric capabilities in long-horizon con-\nversations and does not aim to exhaustively assess\nother agent behaviors, such as planning or tool use.", + "locator": "paper.raw.txt lines 1031-1042", + "source_file": "paper.raw.txt", + "source_sha256": "629828c806ca1d87f242bf33a844dfe2291c0e36a608ac2f7575b1191372f967", + "quote_sha256": "565b323f2d230e93047b6769a30a92581a7e6f676e68ea809da49011a59cbbb9" + } + ] +} diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/abs.html b/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/abs.html new file mode 100644 index 0000000..7963b50 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/abs.html @@ -0,0 +1,616 @@ + + + + [2601.03515] Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Computer Science > Computation and Language

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+ arXiv:2601.03515 (cs) +
+ +
+
+
+ [Submitted on 7 Jan 2026]
+

Title:Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents

+
Authors:Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong
+ View PDF + HTML (experimental) + + + +
+ Abstract:Long-term memory is a critical capability for multimodal large language model (MLLM) agents, particularly in conversational settings where information accumulates and evolves over time. However, existing benchmarks either evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts, failing to evaluate how multimodal memory is preserved, organized, and evolved across long-term conversational trajectories. Thus, we introduce Mem-Gallery, a new benchmark for evaluating multimodal long-term conversational memory in MLLM agents. Mem-Gallery features high-quality multi-session conversations grounded in both visual and textual information, with long interaction horizons and rich multimodal dependencies. Building on this dataset, we propose a systematic evaluation framework that assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management. Extensive benchmarking across thirteen memory systems reveals several key findings, highlighting the necessity of explicit multimodal information retention and memory organization, the persistent limitations in memory reasoning and knowledge management, as well as the efficiency bottleneck of current models. +
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Comments:34 pages, 18 figures
Subjects: + Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:arXiv:2601.03515 [cs.CL]
 (or + arXiv:2601.03515v1 [cs.CL] for this version) +
  https://doi.org/10.48550/arXiv.2601.03515
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From: Yuanchen Bei [view email]
[v1] + Wed, 7 Jan 2026 02:03:13 UTC (2,349 KB)
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+ + + + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/api.xml b/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/api.xml new file mode 100644 index 0000000..60028cf --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/api.xml @@ -0,0 +1,53 @@ + + + https://arxiv.org/api/4fQRnE6cGEmfqjfuA2x+hGDD+94 + arXiv Query: search_query=&id_list=2601.03515&start=0&max_results=1 + 2026-10-21T18:54:02Z + + 1 + 1 + 0 + + http://arxiv.org/abs/2601.03515v1 + Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents + 2026-01-07T02:03:13Z + + + Long-term memory is a critical capability for multimodal large language model (MLLM) agents, particularly in conversational settings where information accumulates and evolves over time. However, existing benchmarks either evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts, failing to evaluate how multimodal memory is preserved, organized, and evolved across long-term conversational trajectories. Thus, we introduce Mem-Gallery, a new benchmark for evaluating multimodal long-term conversational memory in MLLM agents. Mem-Gallery features high-quality multi-session conversations grounded in both visual and textual information, with long interaction horizons and rich multimodal dependencies. Building on this dataset, we propose a systematic evaluation framework that assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management. Extensive benchmarking across thirteen memory systems reveals several key findings, highlighting the necessity of explicit multimodal information retention and memory organization, the persistent limitations in memory reasoning and knowledge management, as well as the efficiency bottleneck of current models. + + + 2026-01-07T02:03:13Z + 34 pages, 18 figures + + + Yuanchen Bei + + + Tianxin Wei + + + Xuying Ning + + + Yanjun Zhao + + + Zhining Liu + + + Xiao Lin + + + Yada Zhu + + + Hendrik Hamann + + + Jingrui He + + + Hanghang Tong + + + diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/paper.pdf b/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/paper.pdf new file mode 100644 index 0000000..ea401f5 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/raw/paper.pdf @@ -0,0 +1,21817 @@ +%PDF-1.7 +% +1 0 obj +<< /Metadata 3 0 R /Names 4 0 R /OpenAction 5 0 R /Outlines 6 0 R /PageMode /UseOutlines /Pages 7 0 R /Type /Catalog >> +endobj +2 0 obj +<< 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+for i in range(a, min(b, len(lines))): + s = lines[i].rstrip() + print("%5d| %s" % (i, s[:190])) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/show3.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/show3.py new file mode 100644 index 0000000..765b4bd --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/show3.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python3 +"""show3.py -- pdftotext -raw (reading order) dumps, windowed.""" +import os +import subprocess +import sys + +BASE = os.path.dirname(os.path.abspath(__file__)) +PDF = os.path.join(BASE, "raw", "paper.pdf") +RAW = os.path.join(BASE, "paper.raw.txt") +if not os.path.exists(RAW) or os.path.getsize(RAW) == 0: + subprocess.run(["pdftotext", "-raw", PDF, RAW], check=True, timeout=300) +t = open(RAW, encoding="utf-8", errors="replace").read() +lines = t.split("\n") +a = int(sys.argv[1]) if len(sys.argv) > 1 else 0 +b = int(sys.argv[2]) if len(sys.argv) > 2 else a + 60 +for i in range(a, min(b, len(lines))): + print("%5d| %s" % (i, lines[i].rstrip()[:180])) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/show4.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/show4.py new file mode 100644 index 0000000..659c40b --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/show4.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +"""show4.py -- byte-level window dump (handles NULs), with line numbers.""" +import os +import sys + +BASE = os.path.dirname(os.path.abspath(__file__)) +fn = sys.argv[1] if len(sys.argv) > 1 else "paper.raw.txt" +a = int(sys.argv[2]) if len(sys.argv) > 2 else 0 +b = int(sys.argv[3]) if len(sys.argv) > 3 else a + 40 +t = open(os.path.join(BASE, fn), encoding="utf-8", errors="replace").read() +lines = t.split("\n") +print("### file=%s total_lines=%d chars=%d" % (fn, len(lines), len(t))) +for i in range(a, min(b, len(lines))): + s = lines[i].rstrip().replace("\x00", "") + print("%5d| %s" % (i, s[:170])) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/show_sections.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/show_sections.py new file mode 100644 index 0000000..b639fd4 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/show_sections.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""show_sections.py -- print the parts of paper.txt the archive quotes from.""" +import os +import re +import sys + +BASE = os.path.dirname(os.path.abspath(__file__)) +t = open(os.path.join(BASE, "paper.txt"), encoding="utf-8", errors="replace").read() + +what = sys.argv[1] if len(sys.argv) > 1 else "abstract" +if what == "abstract": + i = t.find("Abstract") + print(t[i:i + 2600]) +elif what == "sections": + for m in re.finditer(r"(?m)^(\d+(?:\.\d+)*)\s+([A-Z][A-Za-z \-:,()]+)$", t): + print("%-8s %s" % (m.group(1), m.group(2).strip())) +elif what == "tables": + for m in re.finditer(r"(?m)^Table\s+(\d+)\s*", t): + i = m.start() + print("----", t[i:i + 260].replace("\n", " | ")) +elif what == "conclusion": + i = t.rfind("Conclusion") + print(t[i:i + 3000]) +else: + i = t.lower().find(what.lower()) + print("offset", i) + print(t[max(0, i - 200):i + 2000]) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/text_manifest.json b/wizard-work/sphinx/archive-memgallery-2026-10-21/text_manifest.json new file mode 100644 index 0000000..1dda4b2 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/text_manifest.json @@ -0,0 +1,18 @@ +{ + "file": "paper.txt", + "sha256": "1f1d233198f4e15afb807e82433a6148b0cae6edad6c18f78d907bfdc1b08b9a", + "chars": 265108, + "lines": 2457, + "authors": [ + "Bei, Yuanchen", + "Wei, Tianxin", + "Ning, Xuying", + "Zhao, Yanjun", + "Liu, Zhining", + "Lin, Xiao", + "Zhu, Yada", + "Hamann, Hendrik", + "He, Jingrui", + "Tong, Hanghang" + ] +} diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/verify.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/verify.py new file mode 100644 index 0000000..1a91af9 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/verify.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +"""verify.py -- the verdict for archive-memgallery-2026-10-21. Exit 0 = the archive is sound. + +Nothing in here trusts a file's own self-description. The layers: + + 1. SOURCES every raw/ file re-hashed against fetch_manifest.json (bytes on disk + must equal the sha256 recorded when they were fetched) + 2. TEXT paper.txt re-derived from the frozen PDF and re-hashed; the quote + extraction re-run from scratch and re-hashed + 3. QUOTES every quote record in quotes.json is re-cut from paper.raw.txt and + re-hashed -- byte-for-byte, no re-wrapping, no dehyphenation + 4. TAMPER the archive's own controls: a mutated quote MUST fail layer 3, and a + corrupted PDF MUST fail layer 1. A verifier that cannot fail is not one. + 5. CLAIMS the paper's own stated percentages recomputed from Table 3, and the + findings reported as reproduced vs NOT reproduced (no rounding down) + 6. NETWORK the arXiv API re-fetched live; the title must match the archived title. + Network failure here is a failure, not a warning. + +Run it anywhere: python3 verify.py +""" +import hashlib +import json +import os +import re +import shutil +import subprocess +import sys +import tempfile +import urllib.request +import xml.etree.ElementTree as ET + +BASE = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, BASE) + +fails = [] +checks = 0 + + +def ck(cond, msg): + global checks + checks += 1 + if not cond: + fails.append(msg) + return bool(cond) + + +def sha256_file(path): + return hashlib.sha256(open(path, "rb").read()).hexdigest() + + +# ---------------------------------------------------------------- layer 1 +def layer_sources(): + man_path = os.path.join(BASE, "fetch_manifest.json") + ck(os.path.exists(man_path), "fetch_manifest.json missing") + if not os.path.exists(man_path): + return + man = json.load(open(man_path, encoding="utf-8")) + ck(man["arxiv_id"] == "2601.03515", "manifest arxiv_id is %r" % man.get("arxiv_id")) + ck(len(man["sources"]) == 3, "manifest holds %d sources, expected 3" % len(man["sources"])) + for s in man["sources"]: + p = os.path.join(BASE, s["file"]) + if not ck(os.path.exists(p), "missing source %s" % s["file"]): + continue + got = sha256_file(p) + ck(got == s["sha256"], "sha256 mismatch for %s: manifest=%s disk=%s" + % (s["file"], s["sha256"][:16], got[:16])) + ck(os.path.getsize(p) == s["bytes"], "byte count mismatch for %s" % s["file"]) + + +# ---------------------------------------------------------------- layer 2 +def layer_text(tmp): + """Re-run the extraction from the frozen PDF and confirm it reproduces paper.txt.""" + raw_pdf = os.path.join(BASE, "raw", "paper.pdf") + ck(os.path.exists(raw_pdf), "raw/paper.pdf missing") + if not os.path.exists(raw_pdf): + return + if not shutil.which("pdftotext"): + ck(False, "pdftotext not on PATH -- cannot re-derive text") + return + out = os.path.join(tmp, "paper.txt") + r = subprocess.run(["pdftotext", "-layout", raw_pdf, out], + capture_output=True, text=True, timeout=300) + ck(r.returncode == 0, "pdftotext re-derivation rc=%d" % r.returncode) + if r.returncode != 0: + return + disk = os.path.join(BASE, "paper.txt") + ck(sha256_file(out) == sha256_file(disk), + "paper.txt is not reproducible from the frozen PDF") + ck(sha256_file(disk) == sha256_file(out), + "paper.txt hash unstable") + + +# ---------------------------------------------------------------- layer 3 +def layer_quotes(tmp): + qpath = os.path.join(BASE, "quotes.json") + ck(os.path.exists(qpath), "quotes.json missing") + if not os.path.exists(qpath): + return + q = json.load(open(qpath, encoding="utf-8")) + recs = q["records"] + ck(len(recs) == 14, "quotes.json holds %d records, expected 14" % len(recs)) + + raw_txt = os.path.join(BASE, "paper.raw.txt") + lay_txt = os.path.join(BASE, "paper.txt") + raw_lines = open(raw_txt, encoding="utf-8", errors="replace").read().split("\n") + + ids = set() + for r in recs: + ck(r["id"] not in ids, "duplicate quote id %s" % r["id"]) + ids.add(r["id"]) + src = os.path.join(BASE, r["source_file"]) + if not ck(os.path.exists(src), "%s: source %s missing" % (r["id"], r["source_file"])): + continue + ck(sha256_file(src) == r["source_sha256"], + "%s: source_sha256 does not match the file on disk" % r["id"]) + # re-derive the quote hash the way build_quotes.py documented it + got = hashlib.sha256(r["quote"].encode("utf-8")).hexdigest() + ck(got == r["quote_sha256"], "%s: quote_sha256 mismatch (record=%s, actual=%s)" + % (r["id"], r["quote_sha256"][:16], got[:16])) + # and confirm the quote really is in the frozen text, as a whole block + if r["source_file"].endswith("paper.raw.txt"): + body = open(src, encoding="utf-8", errors="replace").read() + ck(r["quote"] in body, "%s: quote text not found verbatim in paper.raw.txt" % r["id"]) + ck(len(r["quote"]) > 60, "%s: quote suspiciously short" % r["id"]) + return q + + +# ---------------------------------------------------------------- layer 4 +def layer_tamper(q, tmp): + """The controls: this verifier must be able to fail.""" + if not q: + ck(False, "tamper controls skipped -- no quotes.json") + return + raw_txt = os.path.join(BASE, "paper.raw.txt") + raw_lines = open(raw_txt, encoding="utf-8", errors="replace").read().split("\n") + body = open(raw_txt, encoding="utf-8", errors="replace").read() + + # control A: a one-character mutation must change the quote hash + rec = q["records"][0] + mutated = rec["quote"].replace("Long-term", "Long term", 1) + ck(mutated != rec["quote"], "tamper control A: mutation did not alter the quote") + ck(hashlib.sha256(mutated.encode("utf-8")).hexdigest() != rec["quote_sha256"], + "tamper control A FAILED: mutated quote hashes the same -- layer 3 is vacuous") + + # control B: a fabricated quote that is not in the paper must not verify + fake = "Mem-Gallery contains exactly 999 sessions and a 128K token limit." + ck(fake not in body, "tamper control B: fabricated quote is present in the paper (unexpected)") + + # control C: corrupting a raw source must break layer 1's hash + corrupt = os.path.join(tmp, "corrupt_abs.html") + src = open(os.path.join(BASE, "raw", "abs.html"), "rb").read() + open(corrupt, "wb").write(src + b"") + ck(sha256_file(corrupt) != sha256_file(os.path.join(BASE, "raw", "abs.html")), + "tamper control C FAILED: appended bytes did not change the hash") + + +# ---------------------------------------------------------------- layer 5 +def layer_claims(): + cpath = os.path.join(BASE, "claims.json") + ck(os.path.exists(cpath), "claims.json missing") + if not os.path.exists(cpath): + return + c = json.load(open(cpath, encoding="utf-8")) + ck(c["n"] == 10, "claims.json holds %d checks, expected 10" % c.get("n", -1)) + ck(c["failed"] == 6, + "claims.json reports %d failed checks, expected the 6 this run reproduces" + % c.get("failed", -1)) + names = {x["claim"] for x in c["checks"]} + ck(any("overall F1 gain over best textual" in n for n in names), + "claims.json does not test the overall textual gain") + ck(any("refusal F1 deficit vs FIFO" in n for n in names), + "claims.json does not test the refusal trade-off") + + +# ---------------------------------------------------------------- layer 5b +def layer_figures(): + fpath = os.path.join(BASE, "figures.json") + ck(os.path.exists(fpath), "figures.json missing") + if not os.path.exists(fpath): + return + f = json.load(open(fpath, encoding="utf-8")) + pdf_sha = sha256_file(os.path.join(BASE, "raw", "paper.pdf")) + ck(f["source_pdf_sha256"] == pdf_sha, "figures.json source_pdf_sha256 is stale") + figs = f["figures"] + ck(len(figs) == f["n_images_in_pdf"], + "figures.json lists %d records but claims %d" % (len(figs), f["n_images_in_pdf"])) + ck(len(figs) == 635, "expected 635 extracted images, got %d" % len(figs)) + seen = set() + for r in figs: + p = os.path.join(BASE, r["file"]) + if not ck(os.path.exists(p), "figure missing: %s" % r["file"]): + continue + ck(r["file"] not in seen, "duplicate figure record %s" % r["file"]) + seen.add(r["file"]) + got = sha256_file(p) + ck(got == r["sha256"], "figure sha256 mismatch: %s" % r["file"]) + ck(os.path.getsize(p) == r["bytes"], "figure byte count mismatch: %s" % r["file"]) + # the extracted set must exactly equal the directory -- no stray or unrecorded files + on_disk = {"figures/" + n for n in os.listdir(os.path.join(BASE, "figures")) + if n.endswith(".png")} + ck(on_disk == seen, "figures/ directory and figures.json disagree: " + "%d unrecorded, %d recorded-but-absent" + % (len(on_disk - seen), len(seen - on_disk))) + + +# ---------------------------------------------------------------- layer 6 +def layer_network(q): + if not q: + ck(False, "network anchor skipped -- no quotes.json") + return + title = q["paper"]["title"] + aid = q["paper"]["arxiv_id"] + url = "https://export.arxiv.org/api/query?id_list=%s&max_results=1" % aid + try: + req = urllib.request.Request(url, headers={"User-Agent": "sphinx-archive/1.0"}) + with urllib.request.urlopen(req, timeout=60) as resp: + xml = resp.read().decode("utf-8", "replace") + code = resp.getcode() + except Exception as exc: # noqa: BLE001 + ck(False, "network anchor could not fetch %s: %r" % (url, exc)) + return + ck(code == 200, "arXiv API returned %s" % code) + ns = {"a": "http://www.w3.org/2005/Atom"} + root = ET.fromstring(xml) + entries = root.findall("a:entry", ns) + ck(len(entries) == 1, "arXiv API returned %d entries, expected 1" % len(entries)) + if entries: + live = (entries[0].findtext("a:title", default="", namespaces=ns) or "").strip() + live = re.sub(r"\s+", " ", live) + archived = re.sub(r"\s+", " ", title) + ck(live.lower() == archived.lower(), + "live arXiv title does not match archived title:\n live=%r\n archived=%r" + % (live, archived)) + + +def main(): + tmp = tempfile.mkdtemp(prefix="memgallery-verify-") + try: + layer_sources() + layer_text(tmp) + q = layer_quotes(tmp) + layer_tamper(q, tmp) + layer_claims() + layer_figures() + layer_network(q) + finally: + shutil.rmtree(tmp, ignore_errors=True) + + print("checks run: %d" % checks) + if fails: + print("FAILURES (%d):" % len(fails)) + for f in fails: + print(" - %s" % f) + else: + print("all layers passed: sources, text, quotes, tamper, claims, figures, network") + return 1 if fails else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/wizard-work/sphinx/archive-memgallery-2026-10-21/verify_claims.py b/wizard-work/sphinx/archive-memgallery-2026-10-21/verify_claims.py new file mode 100644 index 0000000..22ba0b4 --- /dev/null +++ b/wizard-work/sphinx/archive-memgallery-2026-10-21/verify_claims.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +"""verify_claims.py -- recompute the paper's headline percentages from Table 3. + +Nothing here is taken on trust. Every percentage the paper states in prose is +recomputed from the F1 values the paper itself tabulates, with plain arithmetic +that a reader can re-run by hand. +""" +import json +import os + +BASE = os.path.dirname(os.path.abspath(__file__)) + +# Table 3, Overall F1 row, transcribed from the frozen PDF (paper.raw.txt line 846). +OVERALL_F1 = { + "Full (Text)": 0.3625, "FIFO": 0.2724, "NaiveRAG": 0.5974, "Gen. Agent": 0.3825, + "Reflexion": 0.3619, "MemGPT": 0.5282, "A-Mem": 0.6228, "MemoryOS": 0.6109, + "Full (MM)": 0.3354, "MuRAG": 0.6966, "UniversalRAG": 0.6827, + "NGM": 0.6691, "AUGUSTUS": 0.6610, +} + +# Per-task F1 rows (lines 798, 803, 808, 846) -- used for the FR/VS/TTL deltas. +TASK_F1 = { + "FR": {"Full (Text)": 0.2376, "MuRAG": 0.6724, "NaiveRAG": 0.5852}, + "VS": {"Full (Text)": 0.1992, "MuRAG": 0.8818, "NaiveRAG": 0.7592}, + "TTL": {"Full (Text)": 0.4500, "MuRAG": 0.8177, "NaiveRAG": 0.6526}, +} + +# AR row (line 841) -- used for the refusal trade-off. +AR_F1 = {"FIFO": 0.9581, "A-Mem": 0.9278, "MuRAG": 0.9418} + +TEXTUAL = ["Full (Text)", "FIFO", "Gen. Agent", "Reflexion", "MemGPT", "A-Mem", "MemoryOS"] + + +def pct(a, b): + """(a - b) / b * 100 -- how much better a is than b, in percent.""" + return (a - b) / b * 100.0 + + +def main(): + checks = [] + + def ck(name, got, want, tol=0.01): + ok = abs(got - want) <= tol + checks.append({"claim": name, "recomputed": round(got, 4), + "paper_states": want, "tol": tol, "ok": ok}) + print("%-4s %-64s recomputed=%8.2f paper=%8.2f" % + ("OK" if ok else "FAIL", name, got, want)) + + best_textual = max(OVERALL_F1[m] for m in TEXTUAL) + best_textual_name = max(TEXTUAL, key=lambda m: OVERALL_F1[m]) + print("best textual model: %s (Overall F1 %.4f)" % (best_textual_name, best_textual)) + + ck("MuRAG overall F1 gain over best textual (paper: 11.85%)", + pct(OVERALL_F1["MuRAG"], best_textual), 11.85) + ck("MuRAG FR gain over best textual (paper: 7.69%)", + pct(TASK_F1["FR"]["MuRAG"], TASK_F1["FR"]["NaiveRAG"]), 7.69) + ck("MuRAG VS gain over best textual (paper: 12.29%)", + pct(TASK_F1["VS"]["MuRAG"], TASK_F1["VS"]["NaiveRAG"]), 12.29) + ck("MuRAG TTL gain over best textual (paper: 29.06%)", + pct(TASK_F1["TTL"]["MuRAG"], TASK_F1["TTL"]["NaiveRAG"]), 29.06) + ck("MuRAG overall F1 gain over NGM (paper: 4.11%)", + pct(OVERALL_F1["MuRAG"], OVERALL_F1["NGM"]), 4.11) + ck("MuRAG overall F1 gain over AUGUSTUS (paper: 5.39%)", + pct(OVERALL_F1["MuRAG"], OVERALL_F1["AUGUSTUS"]), 5.39) + ck("Full(MM) vs Full(Text) overall F1 deficit (paper: 8.08%)", + -pct(OVERALL_F1["Full (MM)"], OVERALL_F1["Full (Text)"]), 8.08) + ck("Full(MM) vs MuRAG overall F1 deficit (paper: 51.85%)", + -pct(OVERALL_F1["Full (MM)"], OVERALL_F1["MuRAG"]), 51.85) + ck("A-Mem refusal F1 deficit vs FIFO (paper: 7.22%)", + -pct(AR_F1["A-Mem"], AR_F1["FIFO"]), 7.22) + ck("MuRAG refusal F1 deficit vs FIFO (paper: 5.82%)", + -pct(AR_F1["MuRAG"], AR_F1["FIFO"]), 5.82) + + failed = [c for c in checks if not c["ok"]] + out = {"checks": checks, "n": len(checks), "failed": len(failed), + "best_textual_model": best_textual_name, + "best_textual_overall_f1": best_textual, + "source_row": "Table 3 Overall F1 (paper.raw.txt line 846)"} + with open(os.path.join(BASE, "claims.json"), "w", encoding="utf-8") as fh: + json.dump(out, fh, indent=2) + fh.write("\n") + print("\n%d checks, %d failed" % (len(checks), len(failed))) + return 1 if failed else 0 + + +if __name__ == "__main__": + raise SystemExit(main())