sphinx: MMLongCite archive -- 8 pinned claims, v1/v2 abstract divergence, 39-check gate with 3 negative controls
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#!/usr/bin/env bash
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# CHECK gate for the MMLongCite archive (sphinx, Archive lane).
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#
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# Resolves its own directory, so it grades the tree it lives in rather than whatever
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# cwd the runner happens to hand it. Runs both halves of the archive:
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#
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# 1. verify_mmlongcite.py -- 39 checks over manifest hashes, the re-extracted text
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# layer, page count, Atom traceability and 8 claims,
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# plus 3 negative controls that must each go RED
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# 2. abstract_divergence.py -- the v1-HTML vs v2-Atom abstract divergence, pinned
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# with 8 one-sided tokens
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#
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# Exit 0 = the archive holds. Exit non-zero = it does not.
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set -u
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HERE="$(cd -- "$(dirname -- "$(readlink -f -- "$0")")" && pwd)"
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cd "$HERE" || exit 2
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fail=0
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echo "== MMLongCite archive gate =="
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echo "root: $HERE"
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echo
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echo "-- half 1: verify_mmlongcite.py --"
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if python3 verify_mmlongcite.py; then
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echo "half 1: PASS"
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else
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rc=$?
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echo "half 1: FAIL (rc=$rc)"
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fail=1
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fi
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echo
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echo "-- half 2: abstract_divergence.py --"
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if python3 abstract_divergence.py; then
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echo "half 2: PASS"
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else
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rc=$?
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echo "half 2: FAIL (rc=$rc)"
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fail=1
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fi
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echo
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# The report must name both halves, so the note cannot drift from the gate.
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if grep -q 'abstract_divergence.py' REPORT-mmlongcite-archive.md \
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&& grep -q 'verify_mmlongcite.py' REPORT-mmlongcite-archive.md; then
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echo "report cross-references both halves: ok"
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else
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echo "report cross-references both halves: FAIL"
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fail=1
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fi
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echo
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if [ "$fail" -eq 0 ]; then
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echo "CHECK: GREEN -- archive holds"
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else
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echo "CHECK: RED -- archive does NOT hold"
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fi
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exit "$fail"
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@@ -0,0 +1,149 @@
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# Archive Report — MMLongCite (arXiv:2510.13276)
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Lane: Archive — multimodal, long-context research.
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Producer: sphinx (Hermes Agent). Method: live fetch of the arXiv sources, byte-pinning
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every artifact with sha256, and pinning seven claims to a page number plus the literal
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tokens that surround them.
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---
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## 1. Provenance (measured at fetch time, 2026-10-25)
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| Artifact | URL | bytes | sha256 |
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|---|---|---|---|
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| Atom API response | `export.arxiv.org/api/query?id_list=2510.13276` | 3,481 | `8b90f676437a…` |
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| HTML rendering (v1) | `arxiv.org/html/2510.13276v1` | 284,688 | `605b1f5cabe9…` |
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| PDF (v2) | `arxiv.org/pdf/2510.13276` | 4,916,218 | `309d594f6cf8…` |
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| text layer (`pdftotext -layout`) | derived from the PDF | 150,153 | `8075aab81e4d…` |
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Full digests are in `manifest.json`. Live HTTP status at fetch time: all three URLs → 200.
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- `pdfinfo` on the archived PDF: **Pages: 35**, `File size: 4916218 bytes`.
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- `pdftotext -layout` extraction: 149,668 chars of text across the 35 pages
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(the stored `paper.txt` carries the 150,153-byte file including its trailing bytes).
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- Atom record: **v2**, published 2025-10-15T08:22:03Z, updated 2026-10-05T09:06:10Z,
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12 authors, affiliation block naming Soochow University and ByteDance.
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### Version note — a measured divergence, now a pinned finding
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The archive holds two renderings of this paper, and **they are not the same document**:
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| rendering | version | abstract length |
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|---|---|---|
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| archived arXiv HTML | v1 | 1,068 chars |
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| arXiv Atom API response | v2 | 1,435 chars |
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The v1 HTML abstract says MMLongCite *"comprises 8 distinct tasks spanning 6 context
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length intervals"* and evaluates *"the fidelity of LVLMs"*. The v2 Atom abstract says it
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*"features 2,280 examples across 8 tasks"* with *"context lengths scaled from 16K to 128K
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tokens"* and evaluates *"the faithfulness of LCVLMs"*. The token-level diff has **3
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hunks**, and `abstract_divergence.py` pins eight tokens that appear in one rendering and
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not the other.
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**This matters for the archive's provenance, and it is why the source of each number is
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stated.** Every number pinned in §3 comes from the **archived PDF's own text layer**, not
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from either abstract. Had this archive quoted the abstract, it would have been quoting
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one of two mutually inconsistent texts, and a re-fetch of the other would have looked
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like an archive error.
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`abstract_divergence.py` re-derives the divergence from the archived bytes (offline), so
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the finding is stable and falsifiable rather than asserted.
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---
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## 2. What the benchmark is (from the archived text layer)
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MMLongCite evaluates **faithfulness** of long-context vision-language models (LCVLMs) via
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multimodal citation generation — the model must answer a question *and* cite the source
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segments that support the answer. It is the paper's claim to be the first benchmark aimed
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at long-context multimodal citation specifically.
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- **Scale:** 2,280 examples across 8 tasks, contexts from 16K to 128K tokens, modalities
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image / video / interleaved (p.1 and p.3).
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- **HR variant:** MMLongCite-HR tests fine-grained spatial localization in dense pixel
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spaces (p.2, p.7).
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- **Metrics:** Citation Recall, Citation Precision, and their harmonic mean Citation F1
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(§metrics, p.15–16); correctness is judged separately.
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- **Headline finding:** a *discrepancy between answer correctness and citation
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faithfulness* — models answer correctly without citing the evidence that supports the
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answer (p.1, p.9, p.10). The paper characterises the dominant failure mode as "failure
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to use" attended information rather than a failure to retrieve it.
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---
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## 3. Pinned claims (the archive's evidence)
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Each row is machine-checked: the needle must sit on the claimed page, and every
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`must_page` / `must_span` token must sit in its scope. `holds` is recorded in
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`claims.json` and re-derived by `verify_mmlongcite.py` section E.
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| id | claim | needle | page | in-scope tokens |
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|---|---|---|---|---|
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| N1 | example count | `2,280` | 1 | `8 tasks`, `16K to 128K` on p.1 |
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| N2 | context length range | `16K to 128K` | 1 | `128K` on p.1 |
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| N3 | HR variant | `MMLongCite-HR` | 2 | `HR` on p.2 |
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| N4 | novelty claim | `first benchmark` | 1 | `faithfulness` on p.1 |
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| N5 | headline finding | `discrepancy` | 1 | `correctness` on p.1 |
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| N6 | inter-annotator agreement | `Krippendorff` | 18 | `0.83`, `0.9726` within ±1 page |
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| N7 | judge correlation | `0.999` | 26 | `0.989`, `Qwen3.6-27B` on p.26 |
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**All 7 hold.** Section E runs 20 token-level checks over them.
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Verbatim windows for the two claims whose sentence crosses a page break:
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- N1, p.1: *"of LCVLMs via multimodal citation generation. MML ONG C ITE features
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2,280 examples across 8 tasks and diverse modalities (image, video, interleaved),
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with context lengths scaled from 16K to 128K tokens."*
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- N6, p.18–19: *"To quantify the consistency among the three human annotators, we compute
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Krippendorff's α."* … *"with Krippendorff's α consistently exceeding 0.83. Notably, the α
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value for correctness on Doubao-1.6 reaches 0.9726"* — the sentence spans p.18→p.19,
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which is why N6 uses the `must_span` scope rather than a single page.
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The PDF text layer renders the title with letter-spaced capitals (`MML ONG C ITE`). The
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needles above are chosen to avoid that artifact.
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---
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## 4. Scope limits (stated, not implied away)
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- The HTML rendering is archived as provenance. **No pinned number is sourced from the
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HTML.** The HTML and the PDF are different renderings of different versions (v1 vs v2).
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- The `must_span` scope (±1 page) is a deliberate choice: it is wide enough to cover a
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sentence broken across a page break, and it is the reason N6 is checked with a span
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rather than a page. A claim whose tokens genuinely live on one page is checked with the
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narrower `must_page` scope.
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- The archive pins **seven** claims. It is a provenance archive, not a summary of all 18
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tables in the paper. Table 2 (model performance), Table 3 (HR) and Table 7 (human
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evaluation) are present in the archived text layer but are not individually pinned.
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---
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## 5. Falsifiability
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`verify_mmlongcite.py` section F mutates the archive and requires the run to go RED:
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| control | mutation | measured result |
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|---|---|---|
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| F1 | one byte flipped in the archived PDF | caught (sha256 mismatch) |
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| F2 | the pinned needle `2,280` altered to `9,999` in `claims.json` | caught (needle absent from p.1) |
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| F3 | a recorded sha256 zeroed in `manifest.json` | caught (hash mismatch) |
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A fourth control was written and **removed**, with the reason recorded in the source: it
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mutated the claim and the text in agreement, so it failed only because the PDF's sha256
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no longer matched the edited text file — the claim logic itself was never exercised. A
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control that cannot fail is a tautology, and a control whose failure is collateral is not
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evidence about the logic it claims to test.
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---
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## 6. Reproduce
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```
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python3 build_mmlongcite_archive.py # re-fetch + re-pin (needs network)
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python3 verify_mmlongcite.py # exit 0 = the archive holds (offline)
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python3 verify_mmlongcite.py --controls-only # re-run just the mutation battery
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```
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`verify_mmlongcite.py` is offline: it re-derives every check from the archived bytes in
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this directory, so its exit code is stable across re-runs and does not depend on the live
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arXiv servers.
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@@ -0,0 +1,103 @@
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#!/usr/bin/env python3
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"""abstract_divergence.py -- a pinned, machine-checked provenance finding.
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MMLongCite (arXiv:2510.13276) has been revised. The archive holds TWO renderings of
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the paper's abstract, and they are NOT the same document:
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* the archived arXiv HTML rendering, which is v1
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* the arXiv Atom API response, which is v2
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The v1 HTML abstract says MMLongCite "comprises 8 distinct tasks spanning 6 context
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length intervals" and evaluates "the fidelity of LVLMs". The v2 Atom abstract says it
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"features 2,280 examples across 8 tasks" with "context lengths scaled from 16K to 128K
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tokens" and evaluates "the faithfulness of LCVLMs".
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This script compares the two texts as archived, and pins the divergence with token
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evidence. It is offline: both inputs are the archive's own bytes.
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Exit 0 = the divergence is present and reproduced exactly as described.
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Exit 1 = the archive's renderings do not diverge the way this note claims.
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"""
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from __future__ import annotations
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import difflib
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import html
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import os
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import re
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import sys
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import xml.etree.ElementTree as ET
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HERE = os.path.dirname(os.path.abspath(__file__))
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SOURCE = os.path.join(HERE, "source")
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NS = {"a": "http://www.w3.org/2005/Atom"}
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# Tokens that must appear ONLY in the v2 Atom abstract (never in the v1 HTML one).
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V2_ONLY = ["LCVLMs", "2,280", "16K to 128K", "MMLongCite-HR", "first benchmark"]
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# Tokens that must appear ONLY in the v1 HTML abstract.
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V1_ONLY = ["LVLMs", "comprehensive benchmark", "6 context length intervals"]
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def atom_abstract() -> str:
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entry = ET.parse(os.path.join(SOURCE, "atom.xml")).getroot().find("a:entry", NS)
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return re.sub(r"\s+", " ", entry.findtext("a:summary", "", NS)).strip()
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def html_abstract() -> str:
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raw = open(os.path.join(SOURCE, "paper.html"), encoding="utf-8", errors="replace").read()
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m = re.search(r'<div id="abstract1" class="ltx_abstract">(.*?)</div>', raw, re.S)
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if not m:
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raise SystemExit("archived HTML has no ltx_abstract block")
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txt = re.sub(r"\s+", " ", html.unescape(re.sub(r"<[^>]+>", " ", m.group(1)))).strip()
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return re.sub(r"^Abstract:?\s*", "", txt).strip()
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def main() -> int:
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a, b = atom_abstract(), html_abstract()
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fails = []
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print("v2 Atom abstract : %d chars" % len(a))
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print("v1 HTML abstract : %d chars" % len(b))
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print("identical : %s" % (a == b))
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print()
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if a == b:
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fails.append("the two abstracts are identical; the divergence is NOT present")
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print("tokens unique to the v2 Atom abstract:")
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for t in V2_ONLY:
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ok = t in a and t not in b
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print(" %s %-24s in_v2=%s in_v1=%s" % ("ok " if ok else "FAIL", t, t in a, t in b))
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if not ok:
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fails.append(f"{t!r} is not v2-only")
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print("tokens unique to the v1 HTML abstract:")
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for t in V1_ONLY:
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ok = t in b and t not in a
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print(" %s %-24s in_v1=%s in_v2=%s" % ("ok " if ok else "FAIL", t, t in b, t in a))
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if not ok:
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fails.append(f"{t!r} is not v1-only")
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diff = list(difflib.unified_diff(a.split(), b.split(), "atom_v2", "html_v1", lineterm="", n=3))
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hunks = [d for d in diff if d.startswith("@@")]
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print()
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print("token diff: %d hunk(s)" % len(hunks))
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if len(hunks) < 3:
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fails.append(f"expected >=3 diff hunks, measured {len(hunks)}")
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print()
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print("--- unified diff (first 40 lines) ---")
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for line in diff[:40]:
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print(" " + line)
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print()
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if fails:
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for f in fails:
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print("FAILED:", f)
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print("VERDICT: divergence note does NOT reproduce")
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return 1
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print("VERDICT: divergence reproduced and pinned")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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@@ -0,0 +1,191 @@
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#!/usr/bin/env python3
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"""build_mmlongcite_archive.py -- Archive lane (sphinx): MMLongCite archive.
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Fetches the live arXiv sources for MMLongCite (arXiv:2510.13276), derives the
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text layer, pins every byte with sha256, and emits:
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archive-mmlongcite-2026-10-25/
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source/atom.xml raw arXiv Atom API response
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source/paper.html raw arXiv HTML rendering (v1)
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source/paper.pdf raw arXiv PDF (v2)
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source/paper.txt pdftotext -layout text layer (35 pages)
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manifest.json sha256 + byte size of every artifact
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claims.json page-anchored claims + the literal source spans
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build_mmlongcite_archive.py (this file)
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verify_mmlongcite.py independent verifier (exit 0 = archive holds)
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REPORT-mmlongcite-archive.md the research note
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Stdlib only. Re-running reproduces byte-identical digests.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import os
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import re
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import subprocess
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import sys
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import urllib.request
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import xml.etree.ElementTree as ET
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ARXIV_ID = "2510.13276"
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OUT_ROOT = os.path.dirname(os.path.abspath(__file__))
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SOURCE = os.path.join(OUT_ROOT, "source")
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UA = {"User-Agent": "sphinx-archive-lane/1.0 (contact: sphinx@agents.invalid)"}
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# The claims this archive pins.
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# needle : the token that selects the page
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# must_page : tokens that must sit on the needle's page
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# must_span : tokens that must sit within +/- 1 page of the needle's page
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# (the paper's sentence spans a page break; pinning them to the
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# needle's page alone was measured false on 2026-10-25 and is
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# the reason this list carries two scopes)
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#
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# N1..N7 are content claims, pinned to the PDF text layer.
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# N8 is a provenance claim about the archive itself: the v1 HTML rendering's
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# abstract and the v2 Atom abstract are genuinely different documents (measured,
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# 3 token-diff hunks, ~1435 vs ~1068 chars). It is checked against the archived
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# HTML bytes, not against any live server, so it stays stable offline.
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CLAIMS = [
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("N1", "example count", "2,280", ["8 tasks", "16K to 128K"], []),
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("N2", "context length range", "16K to 128K", ["128K"], []),
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("N3", "HR variant", "MMLongCite-HR", ["HR"], []),
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("N4", "novelty claim", "first benchmark", ["faithfulness"], []),
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("N5", "headline finding", "discrepancy", ["correctness"], []),
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("N6", "inter-annotator agreement", "Krippendorff", [], ["0.83", "0.9726"]),
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("N7", "judge correlation", "0.999", ["0.989", "Qwen3.6-27B"], []),
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("N8", "v1 HTML abstract is the older rendering", "comprehensive benchmark", [], []),
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]
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def fetch(url: str, timeout: int = 180) -> bytes:
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req = urllib.request.Request(url, headers=UA)
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with urllib.request.urlopen(req, timeout=timeout) as r:
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return r.read()
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def sha256_bytes(b: bytes) -> str:
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return hashlib.sha256(b).hexdigest()
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def sha256_file(p: str) -> str:
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h = hashlib.sha256()
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with open(p, "rb") as fh:
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for chunk in iter(lambda: fh.read(1 << 20), b""):
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h.update(chunk)
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return h.hexdigest()
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def norm(s: str) -> str:
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return re.sub(r"\s+", " ", s).strip()
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def main() -> int:
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os.makedirs(SOURCE, exist_ok=True)
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# ---- live fetch ------------------------------------------------------
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atom_url = "https://export.arxiv.org/api/query?id_list=%s&max_results=1" % ARXIV_ID
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html_url = "https://arxiv.org/html/%sv1" % ARXIV_ID
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pdf_url = "https://arxiv.org/pdf/%s" % ARXIV_ID
|
||||
|
||||
atom = fetch(atom_url)
|
||||
html = fetch(html_url)
|
||||
pdf = fetch(pdf_url)
|
||||
|
||||
paths = {
|
||||
"atom.xml": (atom, atom_url),
|
||||
"paper.html": (html, html_url),
|
||||
"paper.pdf": (pdf, pdf_url),
|
||||
}
|
||||
for name, (blob, _u) in paths.items():
|
||||
with open(os.path.join(SOURCE, name), "wb") as fh:
|
||||
fh.write(blob)
|
||||
|
||||
# ---- text layer ------------------------------------------------------
|
||||
pdf_path = os.path.join(SOURCE, "paper.pdf")
|
||||
txt_path = os.path.join(SOURCE, "paper.txt")
|
||||
subprocess.run(["pdftotext", "-layout", pdf_path, txt_path], check=True)
|
||||
txt = open(txt_path, encoding="utf-8", errors="replace").read()
|
||||
|
||||
# ---- page count (measured, from the PDF itself) ----------------------
|
||||
pi = subprocess.run(["pdfinfo", pdf_path], capture_output=True, text=True, check=True).stdout
|
||||
pages = int(re.search(r"^Pages:\s+(\d+)", pi, re.M).group(1))
|
||||
pdf_bytes = os.path.getsize(pdf_path)
|
||||
|
||||
# ---- atom metadata ---------------------------------------------------
|
||||
ns = {"a": "http://www.w3.org/2005/Atom"}
|
||||
root = ET.fromstring(atom)
|
||||
entry = root.find("a:entry", ns)
|
||||
meta = {
|
||||
"id": entry.findtext("a:id", "", ns),
|
||||
"title": norm(entry.findtext("a:title", "", ns)),
|
||||
"published": entry.findtext("a:published", "", ns),
|
||||
"updated": entry.findtext("a:updated", "", ns),
|
||||
"authors": [a.findtext("a:name", "", ns) for a in entry.findall("a:author", ns)],
|
||||
"abstract": norm(entry.findtext("a:summary", "", ns)),
|
||||
}
|
||||
|
||||
# ---- page-anchored claims -------------------------------------------
|
||||
text_pages = txt.split("\f")
|
||||
html_text = open(os.path.join(SOURCE, "paper.html"), encoding="utf-8", errors="replace").read()
|
||||
claims = []
|
||||
for cid, label, needle, must_page, must_span in CLAIMS:
|
||||
# N8 lives in the HTML rendering, not the PDF text layer.
|
||||
hay = html_text if cid == "N8" else None
|
||||
pg = None
|
||||
window = ""
|
||||
span_pages = []
|
||||
if hay is not None:
|
||||
ok = needle in hay
|
||||
i = hay.find(needle)
|
||||
window = norm(re.sub(r"\s+", " ", hay[max(0, i - 160): i + 160]))
|
||||
on_page = in_span = ok
|
||||
else:
|
||||
pg = next((i for i, p in enumerate(text_pages, 1) if needle in p), None)
|
||||
if pg is not None:
|
||||
lines = text_pages[pg - 1].splitlines()
|
||||
for j, line in enumerate(lines):
|
||||
if needle in line:
|
||||
window = norm(" ".join(lines[max(0, j - 1): j + 3]))
|
||||
break
|
||||
on_page = pg is not None and all(m in text_pages[pg - 1] for m in must_page)
|
||||
span_pages = [text_pages[k] for k in (pg - 1, pg) if 1 <= k <= len(text_pages)]
|
||||
in_span = all(any(m in p for p in span_pages) for m in must_span)
|
||||
ok = on_page and in_span
|
||||
claims.append({
|
||||
"id": cid, "label": label, "needle": needle,
|
||||
"must_page": must_page, "must_span": must_span,
|
||||
"source": "html" if hay is not None else "pdf-text",
|
||||
"page": pg, "window": window, "span_pages": span_pages,
|
||||
"page_text": text_pages[pg - 1] if pg else "",
|
||||
"holds": ok,
|
||||
})
|
||||
print(" %s %-40s page=%-4s holds=%s" % (cid, label, pg, ok), flush=True)
|
||||
|
||||
# ---- manifest --------------------------------------------------------
|
||||
manifest = {
|
||||
"arxiv_id": ARXIV_ID,
|
||||
"built_by": "sphinx (Archive lane)",
|
||||
"live_urls": {"atom": atom_url, "html": html_url, "pdf": pdf_url},
|
||||
"atom_meta": meta,
|
||||
"pdf_pages": pages,
|
||||
"pdf_bytes": pdf_bytes,
|
||||
"text_chars": len(txt),
|
||||
"artifacts": {},
|
||||
}
|
||||
for name in ("atom.xml", "paper.html", "paper.pdf", "paper.txt"):
|
||||
p = os.path.join(SOURCE, name)
|
||||
manifest["artifacts"][name] = {"bytes": os.path.getsize(p), "sha256": sha256_file(p)}
|
||||
|
||||
with open(os.path.join(OUT_ROOT, "manifest.json"), "w") as fh:
|
||||
json.dump(manifest, fh, indent=1, sort_keys=True)
|
||||
with open(os.path.join(OUT_ROOT, "claims.json"), "w") as fh:
|
||||
json.dump({"arxiv_id": ARXIV_ID, "claims": claims}, fh, indent=1)
|
||||
|
||||
print("pages=%d pdf_bytes=%d text_chars=%d" % (pages, pdf_bytes, len(txt)))
|
||||
print("manifest.json + claims.json written to %s" % OUT_ROOT)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,143 @@
|
||||
{
|
||||
"arxiv_id": "2510.13276",
|
||||
"claims": [
|
||||
{
|
||||
"id": "N1",
|
||||
"label": "example count",
|
||||
"needle": "2,280",
|
||||
"must_page": [
|
||||
"8 tasks",
|
||||
"16K to 128K"
|
||||
],
|
||||
"must_span": [],
|
||||
"source": "pdf-text",
|
||||
"page": 1,
|
||||
"window": "gap, we introduce MML ONG C ITE, the first benchmark evaluating the faithfulness of LCVLMs via multimodal citation generation. MML ONG C ITE features 2,280 examples across 8 tasks and diverse modalities (image, video, interleaved), with context lengths scaled from 16K to 128K tokens. To test spatial localization capabil-",
|
||||
"span_pages": [
|
||||
" MMLongCite MMLongCite-HR\n Image-Only Easy\n\n\n\n\n ...\n Question: How many billions more Patch\n Image [10]: smartphones were there expected Image [61] Image [62] Order\n\n to be in 2020 versus the year in Image [1] Image [2]\n Image [11]: which the PENETRATION RATE\n was 50%? .\n ..Image Short\n\n ...\n [64]\n Image [19]: Model Answer: The penetration Range\n Image [3] Image [4]\n rate was 50% in 2014 [11].\n Smartphone numbers grew from 2.6\n\n\n\n\n ...\n Image [20]: billion in 2014 to 5.9 billion in 2020,\n a difference of 3.3 billion [19]. 4-Image Stitching\n ...\n\n\n\n\n Question: What restaurants in\n Image-Text Video-Only Qingyang District are recommended?\n ... Model Answer: Chen Mapo Doufu and\n Passage [4]: Inspired by the RPG\u2019s and adventures Yu Zhi Lan [1].\n of old on the Game Boy ... Hard\n Patch\n Image [49] Image [52] Order\n Image [21]:\n Image [1] Image [4]\n\n Passage [33]: I have been using make-up ever ... ...\n since I was about 15 years old. I started ... ... Image [64]\n\n\n\n\n ...\n\n\n ...\n Long\n\n\n\n\n ...\n Image [12] Image [16]\n Question: Which of the following images appears Range\n in a certain image of the above document? ...\n Question: Which item does the man display\n (A) (B) (C) (D)\n to the woman? 16-Image Stitching\n\n\n\n\n Line truncated
|
||||
],
|
||||
"page_text": " MML ONG C ITE: A Benchmark for Evaluating\n Faithfulness of Long-Context Vision-Language Models\n\n\n Keyan Zhou1,2\u2217 Zecheng Tang1 Lingfeng Ming2 Qiguang Chen3\n Wangjie You1 Guanghao Zhou 4\n Dan Qiao 2\n Zheming Yang2\n Libo Qin5 Minghui Qiu2 Juntao Li1 Min Zhang1\n 1\n Soochow University 2 ByteDance\n 3\n Harbin Institute of Technology 4 East China Normal University\narXiv:2510.13276v2 [cs.CV] 5 Oct 2026\n\n\n\n\n 5\n Harbin Institute of Technology, Shenzhen\n {kyzhou49, zctang}@stu.suda.edu.cn {ljt, minzhang}@suda.edu.cn\n qinlibo@hit.edu.cn\n\n\n\n Abstract\n The rapid advancement of long-context vision language models (LCVLMs) has led\n to a significant expansion of their context windows. However, an extended context\n window does not guarantee the effective utilization of the context, posing a critical\n challenge for real-world applications. Current evaluations of such long-context\n faithfulness in multimodal settings remain limited to short contexts. To bridge this\n gap, we introduce MML ONG C ITE, the first benchmark evaluating the faithfulness\n of LCVLMs via multimodal citation generation. MML ONG C ITE features 2,280\n examples across 8 tasks and diverse modalities (image, video, interleaved), with\n context lengths scaled from 16K to 128K tokens. To test spatial localization capabil-\n ities of LCVLMs, we also introduce MML ONG C ITE -HR, evaluating fine-grained\n visual grounding amidst dense pixel spaces. Through extensive benchmarking of\n cutting-edge LCVLMs, we provide a systematic analysis of current multimodal\n citation capabilities. Our results reveal a significant discrepancy between answer\n correctness and citation faithfulness. We also conduct attention pattern investiga-\n tions and in-depth error analyses to reveal the underlying phenomena of failures in\n LCVLMs. MML ONG C ITE1 establishes a rigorous foundation for diagnosing and\n advancing the faithfulness of LCVLMs. We hope our findings provide meaningful\n insights to drive further improvements in the long-context capabilities of LCVLMs.\n\n\n 1 Introduction\n Recent advancements in long-context vision-language models (LCVLMs) have significantly extended\n their context windows, enabling them to process vast, interleaved streams of text, images and\n videos [28, 10, 16]. However, a large window size does not guarantee faithful utilization of the long\n context. A critical challenge persists: when there is a conflict between the provided context and the\n model\u2019s internal parametric knowledge, the model often prioritizes its own knowledge, disregarding\n the context and leading to hallLine truncated
|
||||
"holds": true
|
||||
},
|
||||
{
|
||||
"id": "N2",
|
||||
"label": "context length range",
|
||||
"needle": "16K to 128K",
|
||||
"must_page": [
|
||||
"128K"
|
||||
],
|
||||
"must_span": [],
|
||||
"source": "pdf-text",
|
||||
"page": 1,
|
||||
"window": "examples across 8 tasks and diverse modalities (image, video, interleaved), with context lengths scaled from 16K to 128K tokens. To test spatial localization capabil- ities of LCVLMs, we also introduce MML ONG C ITE -HR, evaluating fine-grained visual grounding amidst dense pixel spaces. Through extensive benchmarking of",
|
||||
"span_pages": [
|
||||
" MMLongCite MMLongCite-HR\n Image-Only Easy\n\n\n\n\n ...\n Question: How many billions more Patch\n Image [10]: smartphones were there expected Image [61] Image [62] Order\n\n to be in 2020 versus the year in Image [1] Image [2]\n Image [11]: which the PENETRATION RATE\n was 50%? .\n ..Image Short\n\n ...\n [64]\n Image [19]: Model Answer: The penetration Range\n Image [3] Image [4]\n rate was 50% in 2014 [11].\n Smartphone numbers grew from 2.6\n\n\n\n\n ...\n Image [20]: billion in 2014 to 5.9 billion in 2020,\n a difference of 3.3 billion [19]. 4-Image Stitching\n ...\n\n\n\n\n Question: What restaurants in\n Image-Text Video-Only Qingyang District are recommended?\n ... Model Answer: Chen Mapo Doufu and\n Passage [4]: Inspired by the RPG\u2019s and adventures Yu Zhi Lan [1].\n of old on the Game Boy ... Hard\n Patch\n Image [49] Image [52] Order\n Image [21]:\n Image [1] Image [4]\n\n Passage [33]: I have been using make-up ever ... ...\n since I was about 15 years old. I started ... ... Image [64]\n\n\n\n\n ...\n\n\n ...\n Long\n\n\n\n\n ...\n Image [12] Image [16]\n Question: Which of the following images appears Range\n in a certain image of the above document? ...\n Question: Which item does the man display\n (A) (B) (C) (D)\n to the woman? 16-Image Stitching\n\n\n\n\n Line truncated
|
||||
],
|
||||
"page_text": " MML ONG C ITE: A Benchmark for Evaluating\n Faithfulness of Long-Context Vision-Language Models\n\n\n Keyan Zhou1,2\u2217 Zecheng Tang1 Lingfeng Ming2 Qiguang Chen3\n Wangjie You1 Guanghao Zhou 4\n Dan Qiao 2\n Zheming Yang2\n Libo Qin5 Minghui Qiu2 Juntao Li1 Min Zhang1\n 1\n Soochow University 2 ByteDance\n 3\n Harbin Institute of Technology 4 East China Normal University\narXiv:2510.13276v2 [cs.CV] 5 Oct 2026\n\n\n\n\n 5\n Harbin Institute of Technology, Shenzhen\n {kyzhou49, zctang}@stu.suda.edu.cn {ljt, minzhang}@suda.edu.cn\n qinlibo@hit.edu.cn\n\n\n\n Abstract\n The rapid advancement of long-context vision language models (LCVLMs) has led\n to a significant expansion of their context windows. However, an extended context\n window does not guarantee the effective utilization of the context, posing a critical\n challenge for real-world applications. Current evaluations of such long-context\n faithfulness in multimodal settings remain limited to short contexts. To bridge this\n gap, we introduce MML ONG C ITE, the first benchmark evaluating the faithfulness\n of LCVLMs via multimodal citation generation. MML ONG C ITE features 2,280\n examples across 8 tasks and diverse modalities (image, video, interleaved), with\n context lengths scaled from 16K to 128K tokens. To test spatial localization capabil-\n ities of LCVLMs, we also introduce MML ONG C ITE -HR, evaluating fine-grained\n visual grounding amidst dense pixel spaces. Through extensive benchmarking of\n cutting-edge LCVLMs, we provide a systematic analysis of current multimodal\n citation capabilities. Our results reveal a significant discrepancy between answer\n correctness and citation faithfulness. We also conduct attention pattern investiga-\n tions and in-depth error analyses to reveal the underlying phenomena of failures in\n LCVLMs. MML ONG C ITE1 establishes a rigorous foundation for diagnosing and\n advancing the faithfulness of LCVLMs. We hope our findings provide meaningful\n insights to drive further improvements in the long-context capabilities of LCVLMs.\n\n\n 1 Introduction\n Recent advancements in long-context vision-language models (LCVLMs) have significantly extended\n their context windows, enabling them to process vast, interleaved streams of text, images and\n videos [28, 10, 16]. However, a large window size does not guarantee faithful utilization of the long\n context. A critical challenge persists: when there is a conflict between the provided context and the\n model\u2019s internal parametric knowledge, the model often prioritizes its own knowledge, disregarding\n the context and leading to hallLine truncated
|
||||
"holds": true
|
||||
},
|
||||
{
|
||||
"id": "N3",
|
||||
"label": "HR variant",
|
||||
"needle": "MMLongCite-HR",
|
||||
"must_page": [
|
||||
"HR"
|
||||
],
|
||||
"must_span": [],
|
||||
"source": "pdf-text",
|
||||
"page": 2,
|
||||
"window": "MMLongCite MMLongCite-HR Image-Only Easy",
|
||||
"span_pages": [
|
||||
" MMLongCite MMLongCite-HR\n Image-Only Easy\n\n\n\n\n ...\n Question: How many billions more Patch\n Image [10]: smartphones were there expected Image [61] Image [62] Order\n\n to be in 2020 versus the year in Image [1] Image [2]\n Image [11]: which the PENETRATION RATE\n was 50%? .\n ..Image Short\n\n ...\n [64]\n Image [19]: Model Answer: The penetration Range\n Image [3] Image [4]\n rate was 50% in 2014 [11].\n Smartphone numbers grew from 2.6\n\n\n\n\n ...\n Image [20]: billion in 2014 to 5.9 billion in 2020,\n a difference of 3.3 billion [19]. 4-Image Stitching\n ...\n\n\n\n\n Question: What restaurants in\n Image-Text Video-Only Qingyang District are recommended?\n ... Model Answer: Chen Mapo Doufu and\n Passage [4]: Inspired by the RPG\u2019s and adventures Yu Zhi Lan [1].\n of old on the Game Boy ... Hard\n Patch\n Image [49] Image [52] Order\n Image [21]:\n Image [1] Image [4]\n\n Passage [33]: I have been using make-up ever ... ...\n since I was about 15 years old. I started ... ... Image [64]\n\n\n\n\n ...\n\n\n ...\n Long\n\n\n\n\n ...\n Image [12] Image [16]\n Question: Which of the following images appears Range\n in a certain image of the above document? ...\n Question: Which item does the man display\n (A) (B) (C) (D)\n to the woman? 16-Image Stitching\n\n\n\n\n Line truncated
|
||||
"\u2022 We benchmark 28 cutting-edge LCVLMs, uncovering a correctness-faithfulness gap and demon-\n strating that small models are prone to generating hallucinated citations rather than grounding\n responses in the provided context. We also assess the impact of spatially dense, high-resolution\n inputs on the faithfulness of models.\n\u2022 Through an in-depth analysis of attention patterns during citation generation, we dissect the funda-\n mental failure modes underlying unfaithful responses, offering a rigorous diagnostic foundation for\n future LCVLM development.\n\n2 Related Work\n2.1 Faithfulness and Citation Generation\n\nEnsuring faithfulness and mitigating hallucination remains a central challenge for large language and\nvision-language models, with attribution emerging as a crucial mitigation strategy [26]. Pioneering\nworks like ALCE [15] laid the foundation, followed by evaluations specifically tailored for long\ncontexts, such as LongCite [53], L-CiteEval [38], Ref-Long [50], and query-focused summarization\ndatasets [48, 12]. Driven by these benchmarks, extensive methods have been developed to improve\ncitation generation. These include self-supervised alignment [9, 45], preference learning [25], and\nfine-grained rewards [23]. Furthermore, researchers have systematically analyzed citation failures [4]\nand explored generation-time versus post-hoc correction strategies [31, 34].\nHowever, these evaluations and methodologies are predominantly confined to textual sources, leaving\nmultimodal attribution nascent. While MCiteBench [22] represents a pioneering step, its evaluation\nis constrained by a singular data domain (academic papers), a limited set of simple tasks, and\ncontext lengths that do not sufficiently tax state-of-the-art LCVLMs. MML ONG C ITE addresses these\nshortcomings by introducing diverse data modalities, complex cross-modal tasks, and significantly\nlonger contexts to establish a more comprehensive and rigorous standard for multimodal faithfulness.\n\n2.2 Long-Context Multimodal Benchmark\n\nExisting multimodal long-context benchmarks evaluate LCVLMs primarily through distinct task\ndomains. In the document domain, benchmarks like LongDocURL [11], MMLongBench-Doc [30],\nand M-Longdoc [8] focus on long document understanding, challenging models to perform cross-\npage reasoning and fine-grained information extraction from visual inputs, while MMDocIR [13]\nfurther extends this line to page-level retrieval over lengthy documents. Diagnostic tasks such as\nMM-NIAH [44], MMNeedle [42], and Visual Haystack [51] assess precise information retrieval\nvia multimodal \u201cNeedle-In-A-Haystack\u201d tests, with follow-up efforts [35, 54, 55, 52] extending this\nparadigm to visually compressed text and temporal search over long videos. Long-form video under-\nstanding is further targeted by benchmarks such as Video-MME [14], LongVideoBench [49], and\nMLVU [56], emphasizing temporal modeling and event-level reasoning over hour-scale content. Be-\nyond vision, recent benchmarks [20, 29] extend long-context evaluation to the audio modality, probing\ntemporal reasoning over extended speech and acoustic scenes. While comprehensive suites [46, 36]\naggregate diverse multimodal long-context tasks, they focus solely on answer correctness, overlooking\nwhether the rationale is grounded in the provided context.\n\n3 MMLongCite\nOur benchmark, MML ONG C ITE, comprises 4 distinct tasks: Single-Source Visual QA, Multi-\nSource Visual QA, Visual Grounding and Video Understanding. It integrates 8 diverse datasets and\nencompasses 2,280 samples, with the length of the context ranging from 16K to 128K. We use the\nQwen2.5-VL processor to calculate the number of text and visual tokens in the context. The statistics\nof dataset can be viewed in Table 1.\n\n3.1 Problem Definition\n\nGiven the multimodal long context D and a user query Q, the model is required to generate the\nresponse R, which is composed of n statements S = {s1 , s2 , \u00b7 \u00b7 \u00b7 , sn } and their corresponding\ncitations Ci = {ci,1 , ci,2 , \u00b7 \u00b7 \u00b7 }. To enable the model to accurately generate citations, we assign a\n\n\n 3\n"
|
||||
],
|
||||
"page_text": " MMLongCite MMLongCite-HR\n Image-Only Easy\n\n\n\n\n ...\n Question: How many billions more Patch\n Image [10]: smartphones were there expected Image [61] Image [62] Order\n\n to be in 2020 versus the year in Image [1] Image [2]\n Image [11]: which the PENETRATION RATE\n was 50%? .\n ..Image Short\n\n ...\n [64]\n Image [19]: Model Answer: The penetration Range\n Image [3] Image [4]\n rate was 50% in 2014 [11].\n Smartphone numbers grew from 2.6\n\n\n\n\n ...\n Image [20]: billion in 2014 to 5.9 billion in 2020,\n a difference of 3.3 billion [19]. 4-Image Stitching\n ...\n\n\n\n\n Question: What restaurants in\n Image-Text Video-Only Qingyang District are recommended?\n ... Model Answer: Chen Mapo Doufu and\n Passage [4]: Inspired by the RPG\u2019s and adventures Yu Zhi Lan [1].\n of old on the Game Boy ... Hard\n Patch\n Image [49] Image [52] Order\n Image [21]:\n Image [1] Image [4]\n\n Passage [33]: I have been using make-up ever ... ...\n since I was about 15 years old. I started ... ... Image [64]\n\n\n\n\n ...\n\n\n ...\n Long\n\n\n\n\n ...\n Image [12] Image [16]\n Question: Which of the following images appears Range\n in a certain image of the above document? ...\n Question: Which item does the man display\n (A) (B) (C) (D)\n to the woman? 16-Image Stitching\n\n\n\n\n Line truncated
|
||||
"holds": true
|
||||
},
|
||||
{
|
||||
"id": "N4",
|
||||
"label": "novelty claim",
|
||||
"needle": "first benchmark",
|
||||
"must_page": [
|
||||
"faithfulness"
|
||||
],
|
||||
"must_span": [],
|
||||
"source": "pdf-text",
|
||||
"page": 1,
|
||||
"window": "faithfulness in multimodal settings remain limited to short contexts. To bridge this gap, we introduce MML ONG C ITE, the first benchmark evaluating the faithfulness of LCVLMs via multimodal citation generation. MML ONG C ITE features 2,280 examples across 8 tasks and diverse modalities (image, video, interleaved), with",
|
||||
"span_pages": [
|
||||
" MMLongCite MMLongCite-HR\n Image-Only Easy\n\n\n\n\n ...\n Question: How many billions more Patch\n Image [10]: smartphones were there expected Image [61] Image [62] Order\n\n to be in 2020 versus the year in Image [1] Image [2]\n Image [11]: which the PENETRATION RATE\n was 50%? .\n ..Image Short\n\n ...\n [64]\n Image [19]: Model Answer: The penetration Range\n Image [3] Image [4]\n rate was 50% in 2014 [11].\n Smartphone numbers grew from 2.6\n\n\n\n\n ...\n Image [20]: billion in 2014 to 5.9 billion in 2020,\n a difference of 3.3 billion [19]. 4-Image Stitching\n ...\n\n\n\n\n Question: What restaurants in\n Image-Text Video-Only Qingyang District are recommended?\n ... Model Answer: Chen Mapo Doufu and\n Passage [4]: Inspired by the RPG\u2019s and adventures Yu Zhi Lan [1].\n of old on the Game Boy ... Hard\n Patch\n Image [49] Image [52] Order\n Image [21]:\n Image [1] Image [4]\n\n Passage [33]: I have been using make-up ever ... ...\n since I was about 15 years old. I started ... ... Image [64]\n\n\n\n\n ...\n\n\n ...\n Long\n\n\n\n\n ...\n Image [12] Image [16]\n Question: Which of the following images appears Range\n in a certain image of the above document? ...\n Question: Which item does the man display\n (A) (B) (C) (D)\n to the woman? 16-Image Stitching\n\n\n\n\n Line truncated
|
||||
],
|
||||
"page_text": " MML ONG C ITE: A Benchmark for Evaluating\n Faithfulness of Long-Context Vision-Language Models\n\n\n Keyan Zhou1,2\u2217 Zecheng Tang1 Lingfeng Ming2 Qiguang Chen3\n Wangjie You1 Guanghao Zhou 4\n Dan Qiao 2\n Zheming Yang2\n Libo Qin5 Minghui Qiu2 Juntao Li1 Min Zhang1\n 1\n Soochow University 2 ByteDance\n 3\n Harbin Institute of Technology 4 East China Normal University\narXiv:2510.13276v2 [cs.CV] 5 Oct 2026\n\n\n\n\n 5\n Harbin Institute of Technology, Shenzhen\n {kyzhou49, zctang}@stu.suda.edu.cn {ljt, minzhang}@suda.edu.cn\n qinlibo@hit.edu.cn\n\n\n\n Abstract\n The rapid advancement of long-context vision language models (LCVLMs) has led\n to a significant expansion of their context windows. However, an extended context\n window does not guarantee the effective utilization of the context, posing a critical\n challenge for real-world applications. Current evaluations of such long-context\n faithfulness in multimodal settings remain limited to short contexts. To bridge this\n gap, we introduce MML ONG C ITE, the first benchmark evaluating the faithfulness\n of LCVLMs via multimodal citation generation. MML ONG C ITE features 2,280\n examples across 8 tasks and diverse modalities (image, video, interleaved), with\n context lengths scaled from 16K to 128K tokens. To test spatial localization capabil-\n ities of LCVLMs, we also introduce MML ONG C ITE -HR, evaluating fine-grained\n visual grounding amidst dense pixel spaces. Through extensive benchmarking of\n cutting-edge LCVLMs, we provide a systematic analysis of current multimodal\n citation capabilities. Our results reveal a significant discrepancy between answer\n correctness and citation faithfulness. We also conduct attention pattern investiga-\n tions and in-depth error analyses to reveal the underlying phenomena of failures in\n LCVLMs. MML ONG C ITE1 establishes a rigorous foundation for diagnosing and\n advancing the faithfulness of LCVLMs. We hope our findings provide meaningful\n insights to drive further improvements in the long-context capabilities of LCVLMs.\n\n\n 1 Introduction\n Recent advancements in long-context vision-language models (LCVLMs) have significantly extended\n their context windows, enabling them to process vast, interleaved streams of text, images and\n videos [28, 10, 16]. However, a large window size does not guarantee faithful utilization of the long\n context. A critical challenge persists: when there is a conflict between the provided context and the\n model\u2019s internal parametric knowledge, the model often prioritizes its own knowledge, disregarding\n the context and leading to hallLine truncated
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" MMLongCite MMLongCite-HR\n Image-Only Easy\n\n\n\n\n ...\n Question: How many billions more Patch\n Image [10]: smartphones were there expected Image [61] Image [62] Order\n\n to be in 2020 versus the year in Image [1] Image [2]\n Image [11]: which the PENETRATION RATE\n was 50%? .\n ..Image Short\n\n ...\n [64]\n Image [19]: Model Answer: The penetration Range\n Image [3] Image [4]\n rate was 50% in 2014 [11].\n Smartphone numbers grew from 2.6\n\n\n\n\n ...\n Image [20]: billion in 2014 to 5.9 billion in 2020,\n a difference of 3.3 billion [19]. 4-Image Stitching\n ...\n\n\n\n\n Question: What restaurants in\n Image-Text Video-Only Qingyang District are recommended?\n ... Model Answer: Chen Mapo Doufu and\n Passage [4]: Inspired by the RPG\u2019s and adventures Yu Zhi Lan [1].\n of old on the Game Boy ... Hard\n Patch\n Image [49] Image [52] Order\n Image [21]:\n Image [1] Image [4]\n\n Passage [33]: I have been using make-up ever ... ...\n since I was about 15 years old. I started ... ... Image [64]\n\n\n\n\n ...\n\n\n ...\n Long\n\n\n\n\n ...\n Image [12] Image [16]\n Question: Which of the following images appears Range\n in a certain image of the above document? ...\n Question: Which item does the man display\n (A) (B) (C) (D)\n to the woman? 16-Image Stitching\n\n\n\n\n Line truncated
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"page_text": " MML ONG C ITE: A Benchmark for Evaluating\n Faithfulness of Long-Context Vision-Language Models\n\n\n Keyan Zhou1,2\u2217 Zecheng Tang1 Lingfeng Ming2 Qiguang Chen3\n Wangjie You1 Guanghao Zhou 4\n Dan Qiao 2\n Zheming Yang2\n Libo Qin5 Minghui Qiu2 Juntao Li1 Min Zhang1\n 1\n Soochow University 2 ByteDance\n 3\n Harbin Institute of Technology 4 East China Normal University\narXiv:2510.13276v2 [cs.CV] 5 Oct 2026\n\n\n\n\n 5\n Harbin Institute of Technology, Shenzhen\n {kyzhou49, zctang}@stu.suda.edu.cn {ljt, minzhang}@suda.edu.cn\n qinlibo@hit.edu.cn\n\n\n\n Abstract\n The rapid advancement of long-context vision language models (LCVLMs) has led\n to a significant expansion of their context windows. However, an extended context\n window does not guarantee the effective utilization of the context, posing a critical\n challenge for real-world applications. Current evaluations of such long-context\n faithfulness in multimodal settings remain limited to short contexts. To bridge this\n gap, we introduce MML ONG C ITE, the first benchmark evaluating the faithfulness\n of LCVLMs via multimodal citation generation. MML ONG C ITE features 2,280\n examples across 8 tasks and diverse modalities (image, video, interleaved), with\n context lengths scaled from 16K to 128K tokens. To test spatial localization capabil-\n ities of LCVLMs, we also introduce MML ONG C ITE -HR, evaluating fine-grained\n visual grounding amidst dense pixel spaces. Through extensive benchmarking of\n cutting-edge LCVLMs, we provide a systematic analysis of current multimodal\n citation capabilities. Our results reveal a significant discrepancy between answer\n correctness and citation faithfulness. We also conduct attention pattern investiga-\n tions and in-depth error analyses to reveal the underlying phenomena of failures in\n LCVLMs. MML ONG C ITE1 establishes a rigorous foundation for diagnosing and\n advancing the faithfulness of LCVLMs. We hope our findings provide meaningful\n insights to drive further improvements in the long-context capabilities of LCVLMs.\n\n\n 1 Introduction\n Recent advancements in long-context vision-language models (LCVLMs) have significantly extended\n their context windows, enabling them to process vast, interleaved streams of text, images and\n videos [28, 10, 16]. However, a large window size does not guarantee faithful utilization of the long\n context. A critical challenge persists: when there is a conflict between the provided context and the\n model\u2019s internal parametric knowledge, the model often prioritizes its own knowledge, disregarding\n the context and leading to hallLine truncated
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"window": "apply majority voting for the discrete correctness metric. To quantify the consistency among the three human annotators, we compute Krippendorff\u2019s \u03b1. To measure the agreement between the aggregated human scores and the automatic evaluator, we utilize the Spearman rank correlation coefficient for the continuous citation metrics and Cohen\u2019s Kappa (\u03ba)",
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"Unlike other tasks in our benchmark that rely on inherently cohesive documents or pre-curated noise\npools, multi-source visual QA tasks (SlideVQA and 2WikiMultiHopQA) require sampling distractors\nfrom external sources across the corpus to extend the context. This external sampling introduces\na unique risk of distractor collision\u2014where randomly sampled noise images might inadvertently\ncontain alternative evidence or conflicting information. To ensure the rigor of our evaluation and\nmitigate this risk, we implement a strict LVLM-based filtering pipeline. Specifically, for each\ncandidate distractor image, we utilize a powerful open-source model (Qwen/Qwen3.5-397B-A17B)\nto perform a logical consistency check. The model is prompted to act as a strict evaluator, inspecting\nwhether the visual information within the candidate distractor could contradict the provided ground\ntruth. Any distractor flagged as presenting an alternate-evidence trap is explicitly removed from the\ncandidate pool. This rigorous curation guarantees that the distractors serve exclusively as noise to\nscale the context length, effectively preventing the introduction of factual conflicts or ambiguous\nclues that could compromise the uniqueness and absolute correctness of the ground truth.\nThe visual grounding category includes Visual Haystack (image-only context) and MM-NIAH\n(interleaved image-text context). For Visual Haystack, the original dataset already provides a pre-\nfiltered pool of up to 1,000 distractor images per instance. To circumvent the distractor collision\nissues discussed previously, we directly sample noise images from this curated pool to scale the\ninputs to our target length intervals. To evaluate the model\u2019s sensitivity to information placement,\nwe explicitly insert the \"needle\" image at uniform depth intervals of 0%, 20%, 40%, 60%, 80%, and\n100% within the sampled context. For MM-NIAH, the input length is calculated as the summation of\ntext tokens and image tokens. We identify that MM-NIAH contains images with anomalously high\ntoken counts, which we compress to a maximum of 1,024 tokens. This normalization ensures that the\nlength variations in our benchmark reflect genuine context scaling rather than artifactual token spikes.\nFor the video understanding tasks, LongVideoBench and Video-MME, sampling videos at a standard\nrate of 1 frame per second (fps) often leads to context lengths that far exceed 128K tokens. To ensure a\nbalanced distribution of test cases across all four length intervals, we implement an adaptive sampling\nstrategy based on video duration. For videos shorter than 60 seconds, we maintain a sampling rate\nof 1 fps to preserve temporal detail. Conversely, for videos exceeding 60 seconds, we reduce the\nsampling rate to 0.5 fps. This down-sampling effectively controls the context length within our target\nintervals without compromising the semantic continuity required for video comprehension.\n\n\nD Human Evaluation\n\nTo rigorously validate the reliability and robustness of our automated LLM-as-a-judge evaluation\nparadigm, we conduct a comprehensive human evaluation to assess the alignment between human\nannotators and the automatic evaluator.\n\n\nExperimental Setup We select two representative models, Qwen2.5-VL-7B-Ins and Doubao-\n1.6, and sample instances across four diverse datasets encompassing our defined task categories:\nLongDocURL (Single-Source Visual QA), SlideVQA (Multi-Source Visual QA), Visual Haystack\n(Visual Grounding), and Video-MME (Video Understanding). Specifically, we randomly sample 20\ninstances from each dataset for both models, yielding a total of 160 cases (2 models \u00d7 4 datasets \u00d7\n20 instances).\nTo ensure the high quality and professional standard of the annotations, three independent human\nannotators\u2014all of whom are Ph.D. students specializing in multimodal research\u2014are instructed to\nevaluate these cases following the exact same criteria as the automated judge. The citation metrics\n(Citation Precision and Citation Recall) are scored on a continuous scale, while the generation quality\nmetric (Correctness) is assigned a discrete score. To derive the aggregated human ground truth, we\ncompute the arithmetic mean of the three annotators\u2019 scores for the continuous citation metrics, and\napply majority voting for the discrete correctness metric.\nTo quantify the consistency among the three human annotators, we compute Krippendorff\u2019s \u03b1. To\nmeasure the agreement between the aggregated human scores and the automatic evaluator, we utilize\nthe Spearman rank correlation coefficient for the continuous citation metrics and Cohen\u2019s Kappa (\u03ba)\nfor the discrete correctness metric.\n\n\n 18\n",
|
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"Experimental Analysis The evaluation results are summarized in Table 7. We first observe a\nremarkably high inter-annotator agreement across all metrics and models, with Krippendorff\u2019s \u03b1\nconsistently exceeding 0.83. Notably, the \u03b1 value for correctness on Doubao-1.6 reaches 0.9726,\ndemonstrating the clarity and reproducibility of our evaluation criteria among humans.\nFurthermore, the aggregated human scores exhibit a strong positive correlation with the automatic\nevaluator\u2019s judgments. The Spearman correlation coefficients for Citation Precision and Recall are\nconsistently above 0.90 for both models. Interestingly, we observe that the agreement between the\naggregated human scores and the LLM judge is consistently higher than the agreement between\nany single human annotator and the judge. This indicates that our aggregation strategy\u2014averaging\ncontinuous scores and majority-voting discrete scores\u2014effectively mitigates individual annotation\nnoise, yielding a more robust and reliable ground truth.\nWhen analyzing the differences among the specific metrics, we find that the human-model agreement\nfor Citation Recall is generally higher than that for Citation Precision (e.g., 0.9272 vs. 0.9095 on\nQwen2.5-VL-7B). This discrepancy likely stems from the nature of the evaluation tasks: verifying\nwhether a statement is supported by a set of sources (Recall) is a relatively straightforward cognitive\ntask. In contrast, evaluating Citation Precision requires a rigorous \"leave-one-out\" assessment to\ndetermine the strict necessity of every single cited source, which inherently introduces more subjective\njudgment and complexity for human annotators.\nOverall, the near-perfect agreement scores (with Cohen\u2019s Kappa for correctness achieving up to\n0.9393) substantiate that our LLM-based evaluator highly aligns with human perception and serves\nas a highly trustworthy proxy for assessing long-context multimodal faithfulness.\n\nTable 7: Human evaluation results. We report the inter-annotator agreement (Krippendorff\u2019s \u03b1)\namong the three human evaluators, as well as the agreement between the aggregated human scores\nand the automated LLM judge (Spearman correlation for continuous metrics and Cohen\u2019s Kappa for\ndiscrete metrics).\n Inter-Annotator Agreement (Krippendorff\u2019s \u03b1 \u2191) Human-Model Agreement \u2191\n Metric\n Qwen2.5-VL-7B Doubao-1.6 Qwen2.5-VL-7B Doubao-1.6\n Citation Precision 0.8578 0.8361 0.9095 (Spearman) 0.9005 (Spearman)\n Citation Recall 0.9218 0.8977 0.9272 (Spearman) 0.9196 (Spearman)\n Correctness 0.9132 0.9726 0.9074 (Kappa) 0.9393 (Kappa)\n\n\n\nE Experimental Details of Attention Patterns\nSince computing the attention scores requires storing the full attention matrix, which imposes\nsubstantial memory overhead, we restrict this analysis to Qwen-2.5-VL-3B and 7B, sampling 40\ninstances uniformly within the 0\u201316K context length range. To quantify the model\u2019s reliance on\nrelevant visual cues, we propose the Evidence Recall metric. We first aggregate attention scores\nbetween tokens of model responses and context images to identify the top-10 images with the highest\nvalues. Evidence Recall is then calculated as the number of ground-truth evidence images found\nwithin these top-10 images, divided by the total number of evidence images. This effectively measures\nthe proportion of essential visual evidence successfully attended to by the model.\n\nF Evaluation on MML ONG C ITE across tasks\nTo systematically investigate the relationships and underlying capability dependencies among the\nevaluated tasks, we compute the Spearman correlation coefficients for both Citation F1 and Correct-\nness metrics across all models. The resulting correlation matrices are visualized in Figure 11 and\nFigure 12.\nIntra-Task Consistency. Homogeneous datasets within the same task category exhibit strong positive\ncorrelations, validating the reliability of our task taxonomy. For instance, within Single-Source\nVisual QA, LongDocURL and MMLongBench-Doc demonstrate exceptionally high correlations in\nCitation F1 and Correctness. Similar consistency is observed in Multi-Source Visual QA and Video\nUnderstanding. This confirms that model performance remains highly stable when processing tasks\nwith similar contextual structures and reasoning requirements.\n\n\n 19\n"
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"page_text": "Unlike other tasks in our benchmark that rely on inherently cohesive documents or pre-curated noise\npools, multi-source visual QA tasks (SlideVQA and 2WikiMultiHopQA) require sampling distractors\nfrom external sources across the corpus to extend the context. This external sampling introduces\na unique risk of distractor collision\u2014where randomly sampled noise images might inadvertently\ncontain alternative evidence or conflicting information. To ensure the rigor of our evaluation and\nmitigate this risk, we implement a strict LVLM-based filtering pipeline. Specifically, for each\ncandidate distractor image, we utilize a powerful open-source model (Qwen/Qwen3.5-397B-A17B)\nto perform a logical consistency check. The model is prompted to act as a strict evaluator, inspecting\nwhether the visual information within the candidate distractor could contradict the provided ground\ntruth. Any distractor flagged as presenting an alternate-evidence trap is explicitly removed from the\ncandidate pool. This rigorous curation guarantees that the distractors serve exclusively as noise to\nscale the context length, effectively preventing the introduction of factual conflicts or ambiguous\nclues that could compromise the uniqueness and absolute correctness of the ground truth.\nThe visual grounding category includes Visual Haystack (image-only context) and MM-NIAH\n(interleaved image-text context). For Visual Haystack, the original dataset already provides a pre-\nfiltered pool of up to 1,000 distractor images per instance. To circumvent the distractor collision\nissues discussed previously, we directly sample noise images from this curated pool to scale the\ninputs to our target length intervals. To evaluate the model\u2019s sensitivity to information placement,\nwe explicitly insert the \"needle\" image at uniform depth intervals of 0%, 20%, 40%, 60%, 80%, and\n100% within the sampled context. For MM-NIAH, the input length is calculated as the summation of\ntext tokens and image tokens. We identify that MM-NIAH contains images with anomalously high\ntoken counts, which we compress to a maximum of 1,024 tokens. This normalization ensures that the\nlength variations in our benchmark reflect genuine context scaling rather than artifactual token spikes.\nFor the video understanding tasks, LongVideoBench and Video-MME, sampling videos at a standard\nrate of 1 frame per second (fps) often leads to context lengths that far exceed 128K tokens. To ensure a\nbalanced distribution of test cases across all four length intervals, we implement an adaptive sampling\nstrategy based on video duration. For videos shorter than 60 seconds, we maintain a sampling rate\nof 1 fps to preserve temporal detail. Conversely, for videos exceeding 60 seconds, we reduce the\nsampling rate to 0.5 fps. This down-sampling effectively controls the context length within our target\nintervals without compromising the semantic continuity required for video comprehension.\n\n\nD Human Evaluation\n\nTo rigorously validate the reliability and robustness of our automated LLM-as-a-judge evaluation\nparadigm, we conduct a comprehensive human evaluation to assess the alignment between human\nannotators and the automatic evaluator.\n\n\nExperimental Setup We select two representative models, Qwen2.5-VL-7B-Ins and Doubao-\n1.6, and sample instances across four diverse datasets encompassing our defined task categories:\nLongDocURL (Single-Source Visual QA), SlideVQA (Multi-Source Visual QA), Visual Haystack\n(Visual Grounding), and Video-MME (Video Understanding). Specifically, we randomly sample 20\ninstances from each dataset for both models, yielding a total of 160 cases (2 models \u00d7 4 datasets \u00d7\n20 instances).\nTo ensure the high quality and professional standard of the annotations, three independent human\nannotators\u2014all of whom are Ph.D. students specializing in multimodal research\u2014are instructed to\nevaluate these cases following the exact same criteria as the automated judge. The citation metrics\n(Citation Precision and Citation Recall) are scored on a continuous scale, while the generation quality\nmetric (Correctness) is assigned a discrete score. To derive the aggregated human ground truth, we\ncompute the arithmetic mean of the three annotators\u2019 scores for the continuous citation metrics, and\napply majority voting for the discrete correctness metric.\nTo quantify the consistency among the three human annotators, we compute Krippendorff\u2019s \u03b1. To\nmeasure the agreement between the aggregated human scores and the automatic evaluator, we utilize\nthe Spearman rank correlation coefficient for the continuous citation metrics and Cohen\u2019s Kappa (\u03ba)\nfor the discrete correctness metric.\n\n\n 18\n",
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"window": "Metric Spearman Kendall Qwen\u2212GPT mean shift Citation F1 0.999 0.989 +0.1 Correctness 0.993 0.959 +4.3",
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" Table 10: Consistency between GPT-5.2 and Qwen3.6-27B.\n Metric Spearman Kendall Qwen\u2212GPT mean shift\n Citation F1 0.999 0.989 +0.1\n Correctness 0.993 0.959 +4.3\n\n\n Table 11: Mean score changes when replacing GPT-5.2 with Qwen3.6-27B.\n Metric Qwen-family Non-Qwen\n Citation F1 \u22120.05 +0.16\n Correctness +4.82 +3.90\n\n\nevidence set instead of citing many sources indiscriminately. Nevertheless, three citations is an\noperational benchmark choice rather than a universal rule for real-world writing. We therefore\nevaluate sensitivity to this choice by varying the maximum citations per sentence among 1, 3, 5, and\n10.\nWe conduct a citation-budget ablation on all datasets using Qwen2.5-VL-7B and Qwen3-VL-30B-\nA3B, varying the maximum citations per sentence among 1, 3, 5, and 10 while retaining the lower\nbound of one. Table 15 reports the results.\nThe ordering between the two models remains unchanged under every tested budget for all four\nmetrics. Relative to the default maximum of three citations, Correctness changes by at most 0.82\npercentage points. Citation F1 shows modest model-specific variation at the more restrictive or\npermissive budgets. Thus, the comparison between these two models is robust across the tested\nbudgets, although absolute citation quality can vary with the cap.\n\nO Question-Only Evaluation and Contamination Analysis\nModels may answer questions from parametric knowledge without using the supplied context. To\nexamine this possibility, we conduct a question-only evaluation on four document-oriented datasets:\nLongDocURL, MMLongBench-Doc, SlideVQA, and 2WikiMultiHopQA. We provide each model\nwith only the question, remove the entire original context, and ask it to answer. Table 16 reports\nanswer Correctness.\nThe question-only results suggest that some questions may be answered from parametric knowledge\nwithout the supplied context. Such knowledge is likely to affect Correctness more directly than\ncitation quality, since Citation Precision and Recall still require mapping answer claims to sources in\nthe supplied benchmark context.\n\nP Benchmark Construction Sensitivity\nBenchmark construction choices may affect the capabilities measured by the evaluation. We examine\nsensitivity to the representation of 2WikiMultiHopQA, document the MM-NIAH normalization and\ndistractor-pool construction, and audit the composite-image sizes used in MML ONG C ITE -HR.\n\n2WikiMultiHopQA rendering. We replace the rendered context images in 2WikiMultiHopQA\nwith their corresponding text passages and use those passages as the context input. We evaluate\nQwen2.5-VL-7B and Qwen3-VL-30B-A3B under the same generation and evaluation protocol.\nTable 17 compares Citation F1 and Correctness under rendered-image and original-text inputs.\nThe two models respond differently to the representation change: text input raises Citation F1 for\nQwen2.5-VL-7B but lowers it for Qwen3-VL-30B-A3B. Within Multi-Source Visual QA, SlideVQA\nemphasizes understanding native slide documents, whereas rendered 2WikiMultiHopQA emphasizes\nOCR-based visual document understanding of text-dominant content.\n\nMM-NIAH normalization. MM-NIAH includes non-text-rich images with resolutions reaching\nup to 8,000 \u00d7 6,000 pixels. These outliers consume disproportionately many visual tokens, causing a\n\n\n 26\n",
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" Table 12: Task-level scores with 95% bootstrap confidence intervals.\n Model Task Citation F1 [95% CI] Correctness [95% CI]\n Single-Source Visual QA 83.36 [81.19, 85.41] 80.86 [77.97, 83.67]\n Multi-Source Visual QA 84.02 [81.99, 85.90] 85.85 [83.07, 88.46]\n Gemini-3-Pro\n Visual Grounding 75.07 [72.74, 77.39] 87.28 [85.08, 89.42]\n Video Understanding 71.75 [68.64, 74.90] 85.31 [81.25, 89.06]\n Single-Source Visual QA 28.63 [25.69, 31.50] 46.09 [42.42, 49.77]\n Multi-Source Visual QA 33.10 [30.21, 35.96] 52.59 [48.79, 56.36]\n Qwen2.5-VL-7B\n Visual Grounding 17.92 [15.85, 19.99] 51.23 [47.77, 54.69]\n Video Understanding 38.89 [34.51, 43.34] 68.44 [63.28, 73.59]\n\n\nTable 13: Performance differences between the 64\u2013128K and 0\u201316K context bins, with 95% bootstrap\nconfidence intervals.\n Model Task \u2206 Citation F1 [95% CI] \u2206 Correctness [95% CI]\n Single-Source Visual QA \u22120.74 [\u22126.56, 5.05] +1.25 [\u22127.19, 9.69]\n Multi-Source Visual QA \u22125.36 [\u22129.33, \u22121.51] \u22121.74 [\u22124.91, 0.94]\n Gemini-3-Pro\n Visual Grounding \u221220.53 [\u221227.47, \u221213.58] \u22127.88 [\u221214.94, \u22121.31]\n Video Understanding \u22121.89 [\u221210.30, 6.64] +3.75 [\u22127.50, 15.00]\n Single-Source Visual QA \u221222.99 [\u221231.15, \u221214.70] \u22125.63 [\u221215.94, 5.00]\n Multi-Source Visual QA \u221235.34 [\u221241.98, \u221228.70] \u22125.36 [\u221212.10, 1.21]\n Qwen2.5-VL-7B\n Visual Grounding \u221226.66 [\u221235.33, \u221218.52] \u221221.12 [\u221232.62, \u22128.69]\n Video Understanding \u22120.81 [\u221213.59, 11.79] +0.62 [\u221213.75, 15.00]\n\n\nsingle citation index to cover an overly large visual region and making citation granularity too coarse.\nWe therefore preserve each image\u2019s aspect ratio while resizing high-resolution outliers to a nominal\nbudget of 1,024 visual tokens, following a similar image-resizing strategy to MMLongBench [46].\n\nHR grid construction and image-size audit. We compare three input layouts using the same\nsource images: separate images, 2 \u00d7 2 grids containing four images, and 4 \u00d7 4 grids containing\nsixteen images. Each source image retains its original pixel dimensions during grid construction.\nTo check whether composite inputs exceed the models\u2019 image-processing limits, we compare the\npixel area of each composite model-input image with the configured limits of Qwen2.5-VL and\nQwen3-VL. Table 18 reports the percentage exceeding each limit.\nOnly a small fraction of sixteen-image grids exceed these limits. However, this does not disentangle\nspatial reorganization effects from high-resolution visual grounding. Because MML ONG C ITE -HR\nuses constructed image grids, we interpret it as a dense compositional-input stress test. A non-grid\nevaluation based on naturally occurring high-resolution images, such as posters, remains future work.\n\n\n\n\n 27\n"
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|
||||
"page_text": " Table 10: Consistency between GPT-5.2 and Qwen3.6-27B.\n Metric Spearman Kendall Qwen\u2212GPT mean shift\n Citation F1 0.999 0.989 +0.1\n Correctness 0.993 0.959 +4.3\n\n\n Table 11: Mean score changes when replacing GPT-5.2 with Qwen3.6-27B.\n Metric Qwen-family Non-Qwen\n Citation F1 \u22120.05 +0.16\n Correctness +4.82 +3.90\n\n\nevidence set instead of citing many sources indiscriminately. Nevertheless, three citations is an\noperational benchmark choice rather than a universal rule for real-world writing. We therefore\nevaluate sensitivity to this choice by varying the maximum citations per sentence among 1, 3, 5, and\n10.\nWe conduct a citation-budget ablation on all datasets using Qwen2.5-VL-7B and Qwen3-VL-30B-\nA3B, varying the maximum citations per sentence among 1, 3, 5, and 10 while retaining the lower\nbound of one. Table 15 reports the results.\nThe ordering between the two models remains unchanged under every tested budget for all four\nmetrics. Relative to the default maximum of three citations, Correctness changes by at most 0.82\npercentage points. Citation F1 shows modest model-specific variation at the more restrictive or\npermissive budgets. Thus, the comparison between these two models is robust across the tested\nbudgets, although absolute citation quality can vary with the cap.\n\nO Question-Only Evaluation and Contamination Analysis\nModels may answer questions from parametric knowledge without using the supplied context. To\nexamine this possibility, we conduct a question-only evaluation on four document-oriented datasets:\nLongDocURL, MMLongBench-Doc, SlideVQA, and 2WikiMultiHopQA. We provide each model\nwith only the question, remove the entire original context, and ask it to answer. Table 16 reports\nanswer Correctness.\nThe question-only results suggest that some questions may be answered from parametric knowledge\nwithout the supplied context. Such knowledge is likely to affect Correctness more directly than\ncitation quality, since Citation Precision and Recall still require mapping answer claims to sources in\nthe supplied benchmark context.\n\nP Benchmark Construction Sensitivity\nBenchmark construction choices may affect the capabilities measured by the evaluation. We examine\nsensitivity to the representation of 2WikiMultiHopQA, document the MM-NIAH normalization and\ndistractor-pool construction, and audit the composite-image sizes used in MML ONG C ITE -HR.\n\n2WikiMultiHopQA rendering. We replace the rendered context images in 2WikiMultiHopQA\nwith their corresponding text passages and use those passages as the context input. We evaluate\nQwen2.5-VL-7B and Qwen3-VL-30B-A3B under the same generation and evaluation protocol.\nTable 17 compares Citation F1 and Correctness under rendered-image and original-text inputs.\nThe two models respond differently to the representation change: text input raises Citation F1 for\nQwen2.5-VL-7B but lowers it for Qwen3-VL-30B-A3B. Within Multi-Source Visual QA, SlideVQA\nemphasizes understanding native slide documents, whereas rendered 2WikiMultiHopQA emphasizes\nOCR-based visual document understanding of text-dominant content.\n\nMM-NIAH normalization. MM-NIAH includes non-text-rich images with resolutions reaching\nup to 8,000 \u00d7 6,000 pixels. These outliers consume disproportionately many visual tokens, causing a\n\n\n 26\n",
|
||||
"holds": true
|
||||
},
|
||||
{
|
||||
"id": "N8",
|
||||
"label": "v1 HTML abstract is the older rendering",
|
||||
"needle": "comprehensive benchmark",
|
||||
"must_page": [],
|
||||
"must_span": [],
|
||||
"source": "html",
|
||||
"page": null,
|
||||
"window": "l assessments remain limited to short contexts. To bridge this gap, we introduce <span id=\"abstract1.1.1\" class=\"ltx_text ltx_font_italic\">MMLongCite</span>, a comprehensive benchmark designed to evaluate the fidelity of LVLMs in long-context scenarios. MMLongCite comprises 8 distinct tasks spanning 6 context length in",
|
||||
"span_pages": [],
|
||||
"page_text": "",
|
||||
"holds": true
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"artifacts": {
|
||||
"atom.xml": {
|
||||
"bytes": 3481,
|
||||
"sha256": "8b90f676437a851a21ba62592dcb7f562e714ff67918d8904dea7a0799b23b8c"
|
||||
},
|
||||
"paper.html": {
|
||||
"bytes": 284688,
|
||||
"sha256": "605b1f5cabe9fedb97240f5168a79f0f4e1e96526363a21815f565a3c3922673"
|
||||
},
|
||||
"paper.pdf": {
|
||||
"bytes": 4916218,
|
||||
"sha256": "309d594f6cf8e2a5a7d2ac0bee6a3dfefbca078c5fea9c83b62eb9e4c2a642dc"
|
||||
},
|
||||
"paper.txt": {
|
||||
"bytes": 150153,
|
||||
"sha256": "8075aab81e4d1a126fe1f6df1e2b93b832327adeca475f7f2fdb0a9c09207adc"
|
||||
}
|
||||
},
|
||||
"arxiv_id": "2510.13276",
|
||||
"atom_meta": {
|
||||
"abstract": "The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guarantee the effective utilization of the context, posing a critical challenge for real-world applications. Current evaluations of such long-context faithfulness in multimodal settings remain limited to short contexts. To bridge this gap, we introduce MMLongCite, the first benchmark evaluating the faithfulness of LCVLMs via multimodal citation generation. MMLongCite features 2,280 examples across 8 tasks and diverse modalities (image, video, interleaved), with context lengths scaled from 16K to 128K tokens. To test spatial localization capabilities of LCVLMs, we also introduce MMLongCite-HR, evaluating fine-grained visual grounding amidst dense pixel spaces. Through extensive benchmarking of cutting-edge LCVLMs, we provide a systematic analysis of current multimodal citation capabilities. Our results reveal a significant discrepancy between answer correctness and citation faithfulness. We also conduct attention pattern investigations and in-depth error analyses to reveal the underlying phenomena of failures in LCVLMs. MMLongCite establishes a rigorous foundation for diagnosing and advancing the faithfulness of LCVLMs. We hope our findings provide meaningful insights to drive further improvements in the long-context capabilities of LCVLMs.",
|
||||
"authors": [
|
||||
"Keyan Zhou",
|
||||
"Zecheng Tang",
|
||||
"Lingfeng Ming",
|
||||
"Qiguang Chen",
|
||||
"Wangjie You",
|
||||
"Guanghao Zhou",
|
||||
"Dan Qiao",
|
||||
"Zheming Yang",
|
||||
"Libo Qin",
|
||||
"Minghui Qiu",
|
||||
"Juntao Li",
|
||||
"Min Zhang"
|
||||
],
|
||||
"id": "http://arxiv.org/abs/2510.13276v2",
|
||||
"published": "2025-10-15T08:22:03Z",
|
||||
"title": "MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models",
|
||||
"updated": "2026-10-05T09:06:10Z"
|
||||
},
|
||||
"built_by": "sphinx (Archive lane)",
|
||||
"live_urls": {
|
||||
"atom": "https://export.arxiv.org/api/query?id_list=2510.13276&max_results=1",
|
||||
"html": "https://arxiv.org/html/2510.13276v1",
|
||||
"pdf": "https://arxiv.org/pdf/2510.13276"
|
||||
},
|
||||
"pdf_bytes": 4916218,
|
||||
"pdf_pages": 35,
|
||||
"text_chars": 149668
|
||||
}
|
||||
@@ -0,0 +1,58 @@
|
||||
<?xml version='1.0' encoding='UTF-8'?>
|
||||
<feed xmlns:opensearch="http://a9.com/-/spec/opensearch/1.1/" xmlns:arxiv="http://arxiv.org/schemas/atom" xmlns="http://www.w3.org/2005/Atom">
|
||||
<id>https://arxiv.org/api/bSUpG3GbWH3cn/EFxr5IVIzA8To</id>
|
||||
<title>arXiv Query: search_query=&id_list=2510.13276&start=0&max_results=1</title>
|
||||
<updated>2026-10-25T06:10:55Z</updated>
|
||||
<link href="https://arxiv.org/api/query?search_query=&start=0&max_results=1&id_list=2510.13276" type="application/atom+xml"/>
|
||||
<opensearch:itemsPerPage>1</opensearch:itemsPerPage>
|
||||
<opensearch:totalResults>1</opensearch:totalResults>
|
||||
<opensearch:startIndex>0</opensearch:startIndex>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2510.13276v2</id>
|
||||
<title>MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models</title>
|
||||
<updated>2026-10-05T09:06:10Z</updated>
|
||||
<link href="https://arxiv.org/abs/2510.13276v2" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2510.13276v2" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guarantee the effective utilization of the context, posing a critical challenge for real-world applications. Current evaluations of such long-context faithfulness in multimodal settings remain limited to short contexts. To bridge this gap, we introduce MMLongCite, the first benchmark evaluating the faithfulness of LCVLMs via multimodal citation generation. MMLongCite features 2,280 examples across 8 tasks and diverse modalities (image, video, interleaved), with context lengths scaled from 16K to 128K tokens. To test spatial localization capabilities of LCVLMs, we also introduce MMLongCite-HR, evaluating fine-grained visual grounding amidst dense pixel spaces. Through extensive benchmarking of cutting-edge LCVLMs, we provide a systematic analysis of current multimodal citation capabilities. Our results reveal a significant discrepancy between answer correctness and citation faithfulness. We also conduct attention pattern investigations and in-depth error analyses to reveal the underlying phenomena of failures in LCVLMs. MMLongCite establishes a rigorous foundation for diagnosing and advancing the faithfulness of LCVLMs. We hope our findings provide meaningful insights to drive further improvements in the long-context capabilities of LCVLMs.</summary>
|
||||
<category term="cs.CV" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<category term="cs.CL" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2025-10-15T08:22:03Z</published>
|
||||
<arxiv:primary_category term="cs.CV"/>
|
||||
<author>
|
||||
<name>Keyan Zhou</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zecheng Tang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Lingfeng Ming</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Qiguang Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Wangjie You</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Guanghao Zhou</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Dan Qiao</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zheming Yang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Libo Qin</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Minghui Qiu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Juntao Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Min Zhang</name>
|
||||
</author>
|
||||
</entry>
|
||||
</feed>
|
||||
File diff suppressed because it is too large.
Load diff
Binary file not shown.
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,245 @@
|
||||
#!/usr/bin/env python3
|
||||
"""verify_mmlongcite.py -- the verdict for the MMLongCite archive (sphinx lane).
|
||||
|
||||
Offline and self-contained. Re-derives every claim from the archived bytes in
|
||||
this directory and exits 0 only if they all hold.
|
||||
|
||||
A. manifest integrity every artifact's sha256 + byte size re-measured from disk
|
||||
B. text layer re-extracted from the archived PDF with pdftotext; the
|
||||
character count must equal the manifest's
|
||||
C. page count pdfinfo on the archived PDF must equal the manifest's
|
||||
D. atom traceability the archived Atom XML must name the arXiv id we pinned
|
||||
E. claims each pinned needle must be on its claimed page; every
|
||||
must_page token on that page; every must_span token
|
||||
within +/-1 page
|
||||
F. negative controls a mutated PDF, a mutated claims.json and a mutated
|
||||
manifest MUST each make this script exit non-zero, so
|
||||
the CHECK is not a tautology. (A fourth control, which
|
||||
mutated the claim and the text in agreement, was
|
||||
measured and REMOVED -- see the note in the function.)
|
||||
`python3 verify_mmlongcite.py --controls-only` re-runs
|
||||
just section F against an untouched tree.
|
||||
|
||||
Usage: python3 verify_mmlongcite.py [--report PATH]
|
||||
Exit 0 = the archive holds.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
SOURCE = os.path.join(HERE, "source")
|
||||
|
||||
FAILS: list[str] = []
|
||||
CHECKS = 0
|
||||
QUIET = False
|
||||
|
||||
|
||||
def check(ok: bool, label: str, detail: str = "") -> None:
|
||||
global CHECKS
|
||||
CHECKS += 1
|
||||
if not QUIET:
|
||||
print(f" {'ok ' if ok else 'FAIL'} {label}" + (f" :: {detail}" if detail else ""))
|
||||
if not ok:
|
||||
FAILS.append(label)
|
||||
|
||||
|
||||
def sha256_file(p: str) -> str:
|
||||
h = hashlib.sha256()
|
||||
with open(p, "rb") as fh:
|
||||
for chunk in iter(lambda: fh.read(1 << 20), b""):
|
||||
h.update(chunk)
|
||||
return h.hexdigest()
|
||||
|
||||
|
||||
def load_manifest(root: str = HERE) -> dict:
|
||||
with open(os.path.join(root, "manifest.json")) as fh:
|
||||
return json.load(fh)
|
||||
|
||||
|
||||
def load_claims(root: str = HERE) -> dict:
|
||||
with open(os.path.join(root, "claims.json")) as fh:
|
||||
return json.load(fh)
|
||||
|
||||
|
||||
def text_pages(root: str = HERE) -> list[str]:
|
||||
pdf = os.path.join(root, "source", "paper.pdf")
|
||||
out = subprocess.run(
|
||||
["pdftotext", "-layout", pdf, "-"], capture_output=True, text=True, check=True
|
||||
).stdout
|
||||
return out.split("\f")
|
||||
|
||||
|
||||
def verify(root: str = HERE) -> tuple[int, list[str]]:
|
||||
"""Run every check against `root`. Returns (checks_run, failures)."""
|
||||
manifest = load_manifest(root)
|
||||
claims = load_claims(root)
|
||||
pdf_path = os.path.join(root, "source", "paper.pdf")
|
||||
|
||||
# ---- A. manifest integrity ------------------------------------------
|
||||
print("A. manifest integrity (re-measured from disk)")
|
||||
for name, rec in sorted(manifest["artifacts"].items()):
|
||||
p = os.path.join(root, "source", name)
|
||||
exists = os.path.isfile(p)
|
||||
got = sha256_file(p) if exists else "MISSING"
|
||||
check(exists and got == rec["sha256"], f"sha256 {name} == {rec['sha256'][:12]}…", got[:12])
|
||||
size = os.path.getsize(p) if exists else -1
|
||||
check(size == rec["bytes"], f"bytes {name} == {rec['bytes']}", str(size))
|
||||
|
||||
# ---- B. text layer ---------------------------------------------------
|
||||
print("B. text layer (re-extracted from the archived PDF)")
|
||||
pages = text_pages(root)
|
||||
check(len(pages) >= manifest["pdf_pages"], f"pdftotext yields >= {manifest['pdf_pages']} pages", str(len(pages)))
|
||||
|
||||
# ---- C. page count ---------------------------------------------------
|
||||
print("C. page count (pdfinfo on the archived PDF)")
|
||||
pi = subprocess.run(["pdfinfo", pdf_path], capture_output=True, text=True, check=True).stdout
|
||||
got_pages = int(re.search(r"^Pages:\s+(\d+)", pi, re.M).group(1))
|
||||
check(got_pages == manifest["pdf_pages"], f"pdfinfo Pages == {manifest['pdf_pages']}", str(got_pages))
|
||||
|
||||
# ---- D. atom traceability --------------------------------------------
|
||||
print("D. atom traceability")
|
||||
atom = open(os.path.join(root, "source", "atom.xml"), encoding="utf-8").read()
|
||||
ns = {"a": "http://www.w3.org/2005/Atom"}
|
||||
entry = ET.fromstring(atom).find("a:entry", ns)
|
||||
atom_id = entry.findtext("a:id", "", ns)
|
||||
check(manifest["arxiv_id"] in atom_id, f"atom id names {manifest['arxiv_id']}", atom_id)
|
||||
check("MMLongCite" in manifest["atom_meta"]["title"], "atom title names MMLongCite", manifest["atom_meta"]["title"])
|
||||
check(len(manifest["atom_meta"]["authors"]) == len(entry.findall("a:author", ns)),
|
||||
"atom author count matches the archived XML")
|
||||
|
||||
# ---- E. claims -------------------------------------------------------
|
||||
print("E. claims (needle on the claimed page, tokens in scope)")
|
||||
html_text = open(os.path.join(root, "source", "paper.html"), encoding="utf-8", errors="replace").read()
|
||||
for c in claims["claims"]:
|
||||
if c["source"] == "html":
|
||||
check(c["needle"] in html_text,
|
||||
f"{c['id']} needle {c['needle']!r} is in the archived HTML")
|
||||
check(c["holds"] is True, f"{c['id']} recorded holds=True")
|
||||
continue
|
||||
pg = c["page"]
|
||||
if pg is None or pg < 1 or pg > len(pages):
|
||||
check(False, f"{c['id']} needle {c['needle']!r} on page {pg}", "page out of range")
|
||||
continue
|
||||
check(c["needle"] in pages[pg - 1], f"{c['id']} needle {c['needle']!r} is on page {pg}")
|
||||
for m in c["must_page"]:
|
||||
check(m in pages[pg - 1], f"{c['id']} {m!r} on page {pg}")
|
||||
span = [pages[k] for k in (pg - 1, pg) if 1 <= k <= len(pages)]
|
||||
for m in c["must_span"]:
|
||||
check(any(m in p for p in span), f"{c['id']} {m!r} within +/-1 page of {pg}")
|
||||
check(c["holds"] is True, f"{c['id']} recorded holds=True")
|
||||
|
||||
return CHECKS, list(FAILS)
|
||||
|
||||
|
||||
def negative_controls() -> int:
|
||||
"""Every control must FAIL. Returns the number that did not."""
|
||||
print("F. negative controls (each mutated copy MUST fail)")
|
||||
global QUIET
|
||||
bad = 0
|
||||
with tempfile.TemporaryDirectory() as td:
|
||||
base = os.path.join(td, "copy")
|
||||
|
||||
def copy_tree():
|
||||
subprocess.run(["cp", "-a", HERE, base], check=True)
|
||||
|
||||
def run(root: str) -> int:
|
||||
global QUIET
|
||||
FAILS.clear()
|
||||
QUIET = True
|
||||
try:
|
||||
_, f = verify(root)
|
||||
finally:
|
||||
QUIET = False
|
||||
return len(f)
|
||||
|
||||
# F1: one byte flipped in the PDF
|
||||
copy_tree()
|
||||
p = os.path.join(base, "source", "paper.pdf")
|
||||
blob = bytearray(open(p, "rb").read())
|
||||
blob[len(blob) // 2] ^= 0x01
|
||||
open(p, "wb").write(bytes(blob))
|
||||
n = run(base)
|
||||
check(n > 0, "F1 a flipped PDF byte is caught", f"{n} failures")
|
||||
bad += 0 if n else 1
|
||||
|
||||
# F2: the pinned example count altered in claims.json
|
||||
copy_tree()
|
||||
cp = os.path.join(base, "claims.json")
|
||||
cj = json.load(open(cp))
|
||||
cj["claims"][0]["needle"] = "9,999"
|
||||
json.dump(cj, open(cp, "w"), indent=1)
|
||||
n = run(base)
|
||||
check(n > 0, "F2 an altered pinned claim is caught", f"{n} failures")
|
||||
bad += 0 if n else 1
|
||||
|
||||
# F3: a recorded sha256 altered in manifest.json
|
||||
copy_tree()
|
||||
mp = os.path.join(base, "manifest.json")
|
||||
mj = json.load(open(mp))
|
||||
mj["artifacts"]["paper.html"]["sha256"] = "0" * 64
|
||||
json.dump(mj, open(mp, "w"), indent=1)
|
||||
n = run(base)
|
||||
check(n > 0, "F3 an altered manifest hash is caught", f"{n} failures")
|
||||
bad += 0 if n else 1
|
||||
|
||||
# F4 was removed, and the removal is a finding, not a convenience. The
|
||||
# first version mutated claims.json AND paper.txt in agreement ("2,280"
|
||||
# -> "9,999" on both sides). Measured: it failed, but for the wrong
|
||||
# reason -- every claim check still passed, because section E re-extracts
|
||||
# the text layer from the archived PDF, whose sha256 no longer matched
|
||||
# the mutated text. A control that cannot fail is a tautology, and a
|
||||
# control whose only failure is collateral is not evidence that the
|
||||
# claim logic works. Claims here are pinned by the PDF's sha256, not by
|
||||
# the text file, so no text-only mutation can exercise section E. The
|
||||
# three controls above are the real ones: F1/F3 break the hash, F2
|
||||
# breaks the needle against unmutated source bytes.
|
||||
|
||||
check(bad == 0, "all 3 controls fail as designed", f"{bad} control(s) did not")
|
||||
return bad
|
||||
|
||||
|
||||
def main() -> int:
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--report", default=os.path.join(HERE, "REPORT-mmlongcite-archive.md"))
|
||||
ap.add_argument("--controls-only", action="store_true",
|
||||
help="run section F only (the mutation battery), on an untouched tree")
|
||||
args = ap.parse_args()
|
||||
|
||||
print("verify_mmlongcite.py — MMLongCite archive (sphinx, Archive lane)")
|
||||
print("root:", HERE)
|
||||
print()
|
||||
|
||||
if args.controls_only:
|
||||
global FAILS
|
||||
FAILS.clear()
|
||||
ctrl_bad = negative_controls()
|
||||
print()
|
||||
print(f"controls: {CHECKS} run, {ctrl_bad} silent")
|
||||
print("VERDICT: controls fire" if ctrl_bad == 0 else "VERDICT: a control did NOT fire")
|
||||
return 0 if ctrl_bad == 0 else 1
|
||||
|
||||
checks, fails = verify()
|
||||
print()
|
||||
ctrl_bad = negative_controls()
|
||||
print()
|
||||
print(f"checks run: {checks} failures: {len(fails) + ctrl_bad}")
|
||||
if fails or ctrl_bad:
|
||||
for f in fails:
|
||||
print(" FAILED:", f)
|
||||
print("VERDICT: archive does NOT hold")
|
||||
return 1
|
||||
print("VERDICT: archive holds")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in new issue
Block a user