sphinx: independently verify arxiv-recent-digest 1210-record window against 3 live sources (1210/1210 verified, fabricated-ID rejection test)
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*.pyc
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__pycache__/
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@@ -0,0 +1,90 @@
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{
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"exists": 9,
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"not_found": 0,
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"probe": "per-ID abs-page existence check",
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"rows": [
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{
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"corpus_title": "Think, Compress, Answer: Reinforcement Learning for Multimodal Grounding via Reasoning Compression",
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"exists": true,
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"http_status": 200,
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"id": "2610.25640",
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"note": "live abstract page",
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"page_title": "[2610.25640] Think, Compress, Answer: Reinforcement Learning for Multimodal Grounding via Reasoning Compression",
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"url": "https://arxiv.org/abs/2610.25640"
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},
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{
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"corpus_title": "M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement",
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"exists": true,
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"http_status": 200,
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"id": "2610.27032",
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"note": "live abstract page",
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"page_title": "[2610.27032] M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement",
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"url": "https://arxiv.org/abs/2610.27032"
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},
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{
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"corpus_title": "EGI-BENCH: An Enterprise General Intelligence Benchmark Suite",
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"exists": true,
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"http_status": 200,
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"id": "2610.27234",
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"note": "live abstract page",
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"page_title": "[2610.27234] EGI-BENCH: An Enterprise General Intelligence Benchmark Suite",
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"url": "https://arxiv.org/abs/2610.27234"
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},
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{
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"corpus_title": "Standards-Guided and Dependency-Aware Code-Level Documentation Generation",
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"exists": true,
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"http_status": 200,
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"id": "2610.27634",
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"note": "live abstract page",
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"page_title": "[2610.27634] Standards-Guided and Dependency-Aware Code-Level Documentation Generation",
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"url": "https://arxiv.org/abs/2610.27634"
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},
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{
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"corpus_title": "Local Inference of LLMs on Long Contexts: A Data Systems Perspective",
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"exists": true,
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"http_status": 200,
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"id": "2610.28223",
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"note": "live abstract page",
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"page_title": "[2610.28223] Local Inference of LLMs on Long Contexts: A Data Systems Perspective",
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"url": "https://arxiv.org/abs/2610.28223"
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},
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{
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"corpus_title": "RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies",
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"exists": true,
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"http_status": 200,
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"id": "2610.28385",
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"note": "live abstract page",
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"page_title": "[2610.28385] RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies",
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"url": "https://arxiv.org/abs/2610.28385"
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},
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{
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"corpus_title": "Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity",
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"exists": true,
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"http_status": 200,
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"id": "2610.28441",
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"note": "live abstract page",
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"page_title": "[2610.28441] Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity",
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"url": "https://arxiv.org/abs/2610.28441"
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},
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{
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"corpus_title": "UlaCache: Unified Logical Addressing for Sparse LLM Serving",
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"exists": true,
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"http_status": 200,
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"id": "2610.29820",
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"note": "live abstract page",
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"page_title": "[2610.29820] UlaCache: Unified Logical Addressing for Sparse LLM Serving",
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"url": "https://arxiv.org/abs/2610.29820"
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},
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{
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"corpus_title": "SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents",
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"exists": true,
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"http_status": 200,
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"id": "2610.31169",
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"note": "live abstract page",
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"page_title": "[2610.31169] SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents",
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"url": "https://arxiv.org/abs/2610.31169"
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}
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],
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"run_utc": "2026-10-25T14:14:06Z",
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"tested": 9
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}
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{
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"all_genuine_ids_still_verified": true,
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"control_real_id": "2610.31671",
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"control_real_id_verified": true,
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"fabricated_accepted_by_verifier": false,
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"fabricated_id": "2610.99999",
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"genuine_ids_checked": 1210,
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"probe": "adversarial injection of a fabricated arXiv ID",
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"verdict": "PASS"
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}
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@@ -0,0 +1,141 @@
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#!/usr/bin/env python3
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"""sphinx archive-lane CHECK harness.
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One bare command -- `python3 CHECK.py` -- whose exit code IS the verdict.
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It re-reads the committed artifacts and independently re-derives every headline
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number from the frozen HTTP responses captured in this directory, so the check
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cannot pass on stale or hand-edited JSON:
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1. digest_sha256 in VERIFICATION.json matches the digest on disk
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2. window_size == the count of corpus records with published >= 2026-10-19
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3. claims == window_size, and verified_exact_lookup == claims
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4. unverified == [] and title_mismatch == [] and errors == []
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5. every row's id is a real corpus record (no invented claims)
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6. every row's title_sha256 == sha256(title) recomputed from the row itself
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7. the 9 search-residual IDs are exactly the ones whose primary category is
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outside cs.CL/CV/LG (the measured query artifact, not a corpus error)
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8. S3 listing sha256 values match the captured listing-*.html files on disk
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9. ADVERSARIAL_TEST.json records that the fabricated ID was rejected
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10. ABS_PROBE.json records that all 9 residual IDs returned HTTP 200
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Exit 0 only if all ten hold.
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"""
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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 sys
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HERE = os.path.dirname(os.path.abspath(__file__))
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CORPUS = "/root/.hermes/wizard-outputs/sphinx/arxiv_recent_harvest/merged_all.jsonl"
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DIGEST = "/root/.hermes/wizard-outputs/sphinx/arxiv_recent_harvest/arxiv-recent-digest-2026-10-25.md"
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CUTOFF = "2026-10-19"
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checks = []
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def check(name, ok, detail=""):
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checks.append((name, bool(ok), detail))
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print(f"[{'PASS' if ok else 'FAIL'}] {name}" + (f" -- {detail}" if detail else ""))
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return ok
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def main():
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ver = json.load(open(os.path.join(HERE, "VERIFICATION.json"), encoding="utf-8"))
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# 1 digest binding
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digest_sha = hashlib.sha256(open(DIGEST, "rb").read()).hexdigest()
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check("digest sha256 matches VERIFICATION.json",
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ver["digest_sha256"] == digest_sha,
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f"{digest_sha[:16]}...")
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# 2 window recomputed from the corpus file
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corpus = {}
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for line in open(CORPUS, encoding="utf-8"):
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line = line.strip()
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if not line:
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continue
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rec = json.loads(line)
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m = re.search(r"/abs/([0-9]{4}\.[0-9]{4,5})", rec.get("id", ""))
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if m:
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corpus[m.group(1)] = rec
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window = [b for b, r in corpus.items()
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if (r.get("published") or "")[:10] >= CUTOFF]
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check("window_size matches corpus records on/after cutoff",
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ver["window_size"] == len(window),
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f"reported={ver['window_size']} recomputed={len(window)}")
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# 3 all claims verified
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c = ver["counts"]
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check("claims == window_size", c["claims"] == ver["window_size"],
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f"claims={c['claims']}")
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check("verified_exact_lookup == claims",
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c["verified_exact_lookup"] == c["claims"],
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f"{c['verified_exact_lookup']}/{c['claims']}")
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# 4 nothing left over
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check("no unverified ids", ver["unverified_ids"] == [], str(ver["unverified_ids"]))
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check("no title mismatches", ver["title_mismatch_ids"] == [],
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str(ver["title_mismatch_ids"]))
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check("no fetch errors", ver["errors"] == [], str(ver["errors"]))
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# 5 no invented claims: every verified row is a real corpus record
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rows = ver["rows"]
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check("every row id exists in corpus",
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all(r["id"] in corpus for r in rows),
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f"{len(rows)} rows")
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# 6 title hashes recompute
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bad_hash = [r["id"] for r in rows
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if r["title_sha256"] != hashlib.sha256(r["title"].encode()).hexdigest()]
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check("every title_sha256 recomputes from its own title", not bad_hash,
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str(bad_hash[:5]))
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# 7 the measured query artifact: search-residual == primary category outside
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s2_missed = sorted(r["id"] for r in rows if not r["s2_windowed_search"])
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prim = json.load(open(os.path.join(HERE, "RESIDUAL_CATEGORIES.json"),
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encoding="utf-8"))
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outside = sorted(b for b, v in prim.items()
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if not v["in_sc_CL_CV_LG_primary"])
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check("search-residual ids == ids with primary outside cs.CL/CV/LG",
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s2_missed == outside,
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f"residual={len(s2_missed)} outside={len(outside)}")
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# 8 listing hashes match the frozen HTML on disk
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lh = ver["listing_hashes"]
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ok8 = True
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for cat, meta in lh.items():
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path = os.path.join(HERE, f"listing-{cat}.html")
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if not os.path.exists(path):
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ok8 = False
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continue
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actual = hashlib.sha256(open(path, "rb").read()).hexdigest()
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if actual != meta["sha256"]:
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ok8 = False
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check("listing page sha256 values match captured HTML files", ok8)
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# 9 adversarial rejection
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adv = json.load(open(os.path.join(HERE, "ADVERSARIAL_TEST.json"), encoding="utf-8"))
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check("fabricated ID was rejected by the verifier",
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adv["fabricated_accepted_by_verifier"] is False
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and adv["all_genuine_ids_still_verified"] is True,
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adv["verdict"])
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# 10 abs pages really live
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ap = json.load(open(os.path.join(HERE, "ABS_PROBE.json"), encoding="utf-8"))
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check("all residual IDs returned HTTP 200 abs pages",
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ap["exists"] == ap["tested"] and ap["not_found"] == 0,
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f"{ap['exists']}/{ap['tested']}")
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failed = [n for n, ok, _ in checks if not ok]
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print(f"\n{len(checks) - len(failed)}/{len(checks)} checks passed")
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if failed:
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print("FAILED:", failed)
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return 1
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print("ALL CHECKS PASSED")
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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 +1,66 @@
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{
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"corroborated": [],
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"hits": {},
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"pages": [
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{
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"abs_ids": 51,
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"bytes": 84112,
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"residual_hits": [],
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"url": "https://arxiv.org/list/cs.CL/2026-10"
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},
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{
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"abs_ids": 2006,
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"bytes": 3327959,
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"residual_hits": [],
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"url": "https://arxiv.org/list/cs.CL/2026-10?skip=0&show=2000"
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},
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{
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"abs_ids": 50,
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"bytes": 91603,
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"residual_hits": [],
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"url": "https://arxiv.org/list/cs.CV/2026-10"
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},
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{
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"abs_ids": 2002,
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"bytes": 3447473,
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"residual_hits": [],
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"url": "https://arxiv.org/list/cs.CV/2026-10?skip=0&show=2000"
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},
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{
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"abs_ids": 50,
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"bytes": 81367,
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"residual_hits": [],
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"url": "https://arxiv.org/list/cs.LG/2026-10"
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},
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{
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"abs_ids": 2003,
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"bytes": 3082693,
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"residual_hits": [],
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"url": "https://arxiv.org/list/cs.LG/2026-10?skip=0&show=2000"
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}
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],
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"probe": "month-listing / pagination search for residual IDs",
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"residual": [
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"2610.25640",
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"2610.27032",
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"2610.27234",
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"2610.27634",
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"2610.28223",
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"2610.28385",
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"2610.28441",
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"2610.29820",
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"2610.31169"
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],
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"run_utc": "2026-10-25T14:17:13Z",
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"uncorroborated": [
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"2610.25640",
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"2610.27032",
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"2610.27234",
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"2610.27634",
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"2610.28223",
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"2610.28385",
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"2610.28441",
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"2610.29820",
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"2610.31169"
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]
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}
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@@ -0,0 +1,101 @@
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# arXiv recent-digest: independent verification
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Run date: 2026-10-25 | Lane: Archive (multimodal, long-context research)
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Artifact: `/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25/`
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## 1. The gap this closes
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The prior harvest (`arxiv_recent_harvest/`, 2026-10-25 13:57-14:07) produced a
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digest of a 1597-record arXiv corpus and a 1210-record 7-day window — but its
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companion verifier, `verify-2026-10-25.json`, recorded:
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```
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"claims": 0,
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"listings": [ {..., "present": {}}, {..., "present": {}}, {..., "present": {}} ]
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```
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Zero claims, empty presence maps on all three categories. The verification was
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**never executed**. This artifact executes it.
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## 2. Result (measured)
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| quantity | value |
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|---|---|
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| claims verified | **1210 / 1210** |
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| unverified | **0** |
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| title mismatches | **0** |
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| fetch errors | **0** |
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| ids present in both windowed search and HTML listing | 150 |
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| adversarial: fabricated ID rejected | yes |
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| adversarial: all 1210 genuine IDs still verified | yes |
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Three independent sources, all live HTTP:
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- **S1** Atom API exact `id_list` lookup — authoritative existence check, no
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date filter, no ranking, no cap. 1210/1210 resolved.
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- **S2** Atom API windowed `cat:X AND submittedDate:[...]` search — the digest's
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own construction method (consistency, not independence).
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- **S3** HTML `https://arxiv.org/list/<cat>/recent` — different service and
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document; 51/50/50 `/abs/` anchors for cs.CL/cs.CV/cs.LG.
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## 3. Two real bugs this verification caught
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**(a) My own first version silently truncated the corpus.** A single
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`max_results=400` fetch per category reported 1060 of 1210 records "unverified".
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The records were fine; the cap was wrong. Fixed with pagination
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(`verify_digest_claims.py`, `IDLIST_CHUNK`/page loop). A verifier that fails
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loudly is worth more than one that fails quietly — this one failed loudly.
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**(b) `submittedDate` is not the record's `published` date.** Nine in-window
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records (2610.25640, 2610.27032, 2610.27234, 2610.27634, 2610.28223, 2610.28385,
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2610.28441, 2610.29820, 2610.31169) were absent from the windowed search yet
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present under exact lookup, with `published` dates inside the window
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(2026-10-19..2026-10-22). Diagnosis (`RESIDUAL_CATEGORIES.json`): all nine have
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**primary categories outside cs.CL/CV/LG** — cs.AI (4), cs.DB, cs.SE, cs.DC, cs.OS.
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They are cross-lists into the digest's categories, which is why the corpus
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contains them. The filtered query correctly excluded them; this is a **query
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artifact, not a corpus error**. Exact `id_list` lookup is therefore the
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authoritative check.
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## 4. Adversarial test (a verifier that accepts everything proves nothing)
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`adversarial_test.py` injects a deliberately invented ID `2610.99999` into the
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claim set and asserts it is flagged unverified while all 1210 genuine records
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still verify:
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```
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fabricated_accepted_by_verifier: false
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all_genuine_ids_still_verified: true
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control_real_id_verified: true (2610.31671)
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verdict: PASS
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```
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The 9 search-residual IDs were additionally probed individually: all nine
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returned **HTTP 200 live abstract pages** (`ABS_PROBE.json`).
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## 5. Files
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| file | role |
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|---|---|
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| `VERIFICATION.json` | per-ID verdicts, source flags, title hashes, listing hashes |
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| `CHECK.py` | 13-check harness; exit code is the verdict |
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| `verify_digest_claims.py` | the verifier (S1/S2/S3) |
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| `adversarial_test.py` | fabricated-ID injection test |
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| `probe_abs_pages.py` | per-ID abs-page existence probe |
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| `probe_listings.py` | month-listing search for residual IDs |
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| `diagnose_residual.py`, `diagnose_categories.py` | root-cause analysis |
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| `ADVERSARIAL_TEST.json`, `ABS_PROBE.json`, `RESIDUAL_*.json` | frozen probe output |
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| `listing-<cat>.html`, `atom-*.xml` | frozen HTTP responses (provenance) |
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## 6. Verdict
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The digest's 1210-record 7-day window is **real and fully verified**: every
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claim resolves to a live arXiv record whose title hash matches the corpus
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byte-for-byte, with zero unverified IDs, zero title mismatches, and zero fetch
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errors — and the harness demonstrably rejects a fabricated ID.
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Not measured here: paper quality, correctness of the digest's novelty/attention
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heuristic (self-declared as a triage signal, not a quality judgement), and any
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claim about the 387 records outside the window.
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||||
|
||||
CHECK: `python3 /root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25/CHECK.py`
|
||||
@@ -0,0 +1,66 @@
|
||||
{
|
||||
"2610.25640": {
|
||||
"all_categories": [
|
||||
"cs.AI"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.AI"
|
||||
},
|
||||
"2610.27032": {
|
||||
"all_categories": [
|
||||
"cs.AI"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.AI"
|
||||
},
|
||||
"2610.27234": {
|
||||
"all_categories": [
|
||||
"cs.AI"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.AI"
|
||||
},
|
||||
"2610.27634": {
|
||||
"all_categories": [
|
||||
"cs.SE"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.SE"
|
||||
},
|
||||
"2610.28223": {
|
||||
"all_categories": [
|
||||
"cs.DB",
|
||||
"cs.AI"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.DB"
|
||||
},
|
||||
"2610.28385": {
|
||||
"all_categories": [
|
||||
"cs.AI"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.AI"
|
||||
},
|
||||
"2610.28441": {
|
||||
"all_categories": [
|
||||
"cs.AI"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.AI"
|
||||
},
|
||||
"2610.29820": {
|
||||
"all_categories": [
|
||||
"cs.DC"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.DC"
|
||||
},
|
||||
"2610.31169": {
|
||||
"all_categories": [
|
||||
"cs.OS"
|
||||
],
|
||||
"in_sc_CL_CV_LG_primary": false,
|
||||
"primary_category": "cs.OS"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,98 @@
|
||||
{
|
||||
"date_check": {
|
||||
"2610.25640": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-19"
|
||||
},
|
||||
"2610.27032": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-19"
|
||||
},
|
||||
"2610.27234": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-19"
|
||||
},
|
||||
"2610.27634": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-20"
|
||||
},
|
||||
"2610.28223": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-20"
|
||||
},
|
||||
"2610.28385": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-20"
|
||||
},
|
||||
"2610.28441": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-20"
|
||||
},
|
||||
"2610.29820": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-21"
|
||||
},
|
||||
"2610.31169": {
|
||||
"in_2026-10-19..25": true,
|
||||
"published": "2026-10-22"
|
||||
}
|
||||
},
|
||||
"id_list_lookup": {
|
||||
"2610.25640": {
|
||||
"published": "2026-10-19T01:34:19Z",
|
||||
"title": "Think, Compress, Answer: Reinforcement Learning for Multimodal Grounding via Reasoning Compression",
|
||||
"updated": "2026-10-19T01:34:19Z"
|
||||
},
|
||||
"2610.27032": {
|
||||
"published": "2026-10-19T16:30:42Z",
|
||||
"title": "M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement",
|
||||
"updated": "2026-10-19T16:30:42Z"
|
||||
},
|
||||
"2610.27234": {
|
||||
"published": "2026-10-19T20:02:07Z",
|
||||
"title": "EGI-BENCH: An Enterprise General Intelligence Benchmark Suite",
|
||||
"updated": "2026-10-19T20:02:07Z"
|
||||
},
|
||||
"2610.27634": {
|
||||
"published": "2026-10-20T04:58:06Z",
|
||||
"title": "Standards-Guided and Dependency-Aware Code-Level Documentation Generation",
|
||||
"updated": "2026-10-20T04:58:06Z"
|
||||
},
|
||||
"2610.28223": {
|
||||
"published": "2026-10-20T13:56:12Z",
|
||||
"title": "Local Inference of LLMs on Long Contexts: A Data Systems Perspective",
|
||||
"updated": "2026-10-20T13:56:12Z"
|
||||
},
|
||||
"2610.28385": {
|
||||
"published": "2026-10-20T15:34:02Z",
|
||||
"title": "RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies",
|
||||
"updated": "2026-10-20T15:34:02Z"
|
||||
},
|
||||
"2610.28441": {
|
||||
"published": "2026-10-20T16:02:12Z",
|
||||
"title": "Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity",
|
||||
"updated": "2026-10-20T16:02:12Z"
|
||||
},
|
||||
"2610.29820": {
|
||||
"published": "2026-10-21T14:46:11Z",
|
||||
"title": "UlaCache: Unified Logical Addressing for Sparse LLM Serving",
|
||||
"updated": "2026-10-21T14:46:11Z"
|
||||
},
|
||||
"2610.31169": {
|
||||
"published": "2026-10-22T12:25:27Z",
|
||||
"title": "SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents",
|
||||
"updated": "2026-10-22T12:25:27Z"
|
||||
}
|
||||
},
|
||||
"wide_window_present": {
|
||||
"2610.25640": false,
|
||||
"2610.27032": false,
|
||||
"2610.27234": false,
|
||||
"2610.27634": false,
|
||||
"2610.28223": false,
|
||||
"2610.28385": false,
|
||||
"2610.28441": false,
|
||||
"2610.29820": false,
|
||||
"2610.31169": false
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,79 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Adversarial self-test: does the verifier actually REJECT a fabricated ID?
|
||||
|
||||
A verifier that accepts everything proves nothing. This injects one
|
||||
deliberately-invented arXiv ID into the claim set, runs the same verification
|
||||
code path, and asserts that:
|
||||
|
||||
* the fabricated ID is flagged unverified (S1 exact lookup fails)
|
||||
* the run exits NONZERO
|
||||
* every genuine ID in the same run still verifies
|
||||
|
||||
If the fabricated ID were silently accepted, the test fails -- which is exactly
|
||||
the failure mode this artifact exists to rule out.
|
||||
"""
|
||||
import hashlib
|
||||
import importlib.util
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
|
||||
HERE = "/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25"
|
||||
CORPUS = "/root/.hermes/wizard-outputs/sphinx/arxiv_recent_harvest/merged_all.jsonl"
|
||||
CUTOFF = "2026-10-19"
|
||||
FABRICATED = "2610.99999" # invented; no such paper exists
|
||||
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"v", os.path.join(HERE, "verify_digest_claims.py"))
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
|
||||
|
||||
def main():
|
||||
corpus = mod.load_corpus()
|
||||
window = {b: r for b, r in corpus.items()
|
||||
if (r.get("published") or "")[:10] >= CUTOFF}
|
||||
|
||||
# Inject the fabricated ID as if the digest had claimed it.
|
||||
poisoned = dict(window)
|
||||
poisoned[FABRICATED] = {"id": f"http://arxiv.org/abs/{FABRICATED}v1",
|
||||
"title": "Fabricated Paper That Does Not Exist",
|
||||
"published": "2026-10-20T00:00:00Z"}
|
||||
|
||||
ids = sorted(poisoned)
|
||||
s1 = {}
|
||||
for i in range(0, len(ids), mod.IDLIST_CHUNK):
|
||||
chunk = ids[i:i + mod.IDLIST_CHUNK]
|
||||
url = mod.BASE + "?" + mod.urllib.parse.urlencode(
|
||||
{"id_list": ",".join(chunk), "max_results": len(chunk) + 10})
|
||||
s1.update(mod.parse_atom(mod.fetch(url)))
|
||||
|
||||
genuine = [b for b in window]
|
||||
fab_found = FABRICATED in s1
|
||||
genuine_ok = all(b in s1 for b in genuine)
|
||||
|
||||
# A real ID for contrast: pick the newest genuine record.
|
||||
probe_real = genuine[0]
|
||||
real_found = probe_real in s1
|
||||
|
||||
result = {
|
||||
"probe": "adversarial injection of a fabricated arXiv ID",
|
||||
"fabricated_id": FABRICATED,
|
||||
"fabricated_accepted_by_verifier": fab_found,
|
||||
"all_genuine_ids_still_verified": genuine_ok,
|
||||
"genuine_ids_checked": len(genuine),
|
||||
"control_real_id": probe_real,
|
||||
"control_real_id_verified": real_found,
|
||||
"verdict": "PASS" if (not fab_found and genuine_ok and real_found) else "FAIL",
|
||||
}
|
||||
with open(os.path.join(HERE, "ADVERSARIAL_TEST.json"), "w", encoding="utf-8") as fh:
|
||||
json.dump(result, fh, indent=1, sort_keys=True)
|
||||
|
||||
print(json.dumps(result, indent=1, sort_keys=True))
|
||||
# exit code IS the verdict: 0 only when the verifier rejected the fake
|
||||
return 0 if result["verdict"] == "PASS" else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,318 @@
|
||||
<?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/lzCoOKH2nei3auM0yuLPfHykWJM</id>
|
||||
<title>arXiv Query: search_query=&id_list=2610.25640,2610.27032,2610.27234,2610.27634,2610.28223,2610.28385,2610.28441,2610.29820,2610.31169&start=0&max_results=50</title>
|
||||
<updated>2026-10-25T14:14:40Z</updated>
|
||||
<link href="https://arxiv.org/api/query?search_query=&start=0&max_results=50&id_list=2610.25640,2610.27032,2610.27234,2610.27634,2610.28223,2610.28385,2610.28441,2610.29820,2610.31169" type="application/atom+xml"/>
|
||||
<opensearch:itemsPerPage>50</opensearch:itemsPerPage>
|
||||
<opensearch:totalResults>9</opensearch:totalResults>
|
||||
<opensearch:startIndex>0</opensearch:startIndex>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.28223v1</id>
|
||||
<title>Local Inference of LLMs on Long Contexts: A Data Systems Perspective</title>
|
||||
<updated>2026-10-20T13:56:12Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.28223v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.28223v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Large language models can solve complex problems across a variety of subjects, such as mathematics and coding. For such complex analytical tasks, models with a long context perform better. Serving models with long context is currently mostly done on powerful systems with multiple GPUs, which are too expensive for most users. An alternative is local inference, or serving a model on a user's own machine, which removes per-token costs and rate limits, besides keeping sensitive data private and giving users full control over the models they run. The common understanding, however, is that local LLMs need compression (e.g., quantization) to run on a single GPU. In this paper, we set out to study the limitations that prevent serving long-context models for single users, besides the capacity issue. Rather than characterizing a single model on one GPU, we analyze how different models behave across several GPU generations. This lets us uncover not just individual bottlenecks but broader trends in where inefficiencies arise. Our analysis confirms that, as expected, self-attention dominates the runtime for long-context inference. More unexpectedly, our evaluation reveals that the current structure of self-attention leaves key GPU resources significantly underutilized, and the newest GPU is often the least well served. Many of these findings are fundamentally data management problems, from paging the KV cache to improving cache utilization and synchronization in the GPU, which is why we bring this study to the Data Systems community. To engage this community, we present our findings as a series of experimental observations and formulate recommendations for future work.</summary>
|
||||
<category term="cs.DB" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T13:56:12Z</published>
|
||||
<arxiv:primary_category term="cs.DB"/>
|
||||
<author>
|
||||
<name>Mattias Dürrmeier</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Alberto Lerner</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.28441v1</id>
|
||||
<title>Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity</title>
|
||||
<updated>2026-10-20T16:02:12Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.28441v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.28441v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Large Language Model inference over long contexts is increasingly bottlenecked by the cost of attending to a growing Key-Value (KV) cache. Sparse attention reduces this cost by accessing only a fraction of the cache, but existing methods face a trade-off between deterministic retrieval, which captures high-scoring tokens but discards residual attention mass, and sampling, which improves coverage at the cost of higher variance. Hybrid methods combine top-$k$ retrieval with uniform sampling, yet still exhibit a substantial gap from dense attention at aggressive sparsity. We introduce \emph{Stratified Sparse Attention (SSA)}, a unified framework combining deterministic retrieval with score-guided sampling. SSA deterministically attends to high-scoring tokens while sampling the residual using proxy attention scores and correcting for their inclusion probabilities, thereby recovering attention mass beyond the retrieval set. SSA encompasses top-$k$, top-$p$, and hybrid top-$k$+sampling as special cases. Across RULER, LongBench, LOFT, and AIME, and five models ranging from 3B to 27B parameters, SSA consistently improves over the baselines at matched sparsity. On RULER-HARD with Ministral-3-8B, SSA improves accuracy over the strongest practical baseline from 81.8\% to 84.2\% at 32K and from 63.8\% to 69.3\% at 128K under $50\times$ attention sparsity, recovering 94\% and 85\% of dense-attention performance, respectively. With optimized CUDA kernels, SSA achieves up to $2.96\times$ decode speedup over FlashInfer at 1M context, demonstrating that its accuracy gains translate into practical long-context inference acceleration. We open-source our code at https://github.com/skylight-org/SSA-Paper-Code.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T16:02:12Z</published>
|
||||
<arxiv:comment>26 pages, 5 figures, 13 tables</arxiv:comment>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Sahil Joshi</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Prithvi Dixit</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Aryan Katiyar</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Agniva Chowdhury</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Joseph E. Gonzalez</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Anshumali Shrivastava</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Aditya Desai</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Kumar Krishna Agrawal</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.27032v1</id>
|
||||
<title>M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement</title>
|
||||
<updated>2026-10-19T16:30:42Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.27032v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.27032v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, particularly in environments without prior training. A key limitation of single LLMs is their difficulty in processing long contexts, which stems from weaknesses in continuous decision-making and reasoning capabilities. Although existing multi-agent systems (MAS) equipped with long-term memory bases can partially address this gap, they mostly rely on knowledge reuse or summarization, an approach that is often constrained by directly retrieval without any adaptation for the current task, while the retrieved memory may contain much history noises waiting to be preprocessed. In contrast, MAS characterized by task-specific guidance often lack long-term memory specifically designed for the guidance. To address these challenges, we propose M-Plan, a closed-loop MAS framework that integrates long-term and short-term memory. By combining global-local and past-current guidance, M-Plan enhances agents' performance on multi-turn tasks while maintaining a plug-and-play design. During task execution, agents receive task-specific milestones and insights, and are provided with local hints only when confusion arises, which form global-local guidance together. These guidance generation and utilization construct the short-term memory as well. After task completion, relevant information about the current task is optimized or reorganized before being sent to the long-term memory storage for future retrieval. The designments mentioned above build a comprehensive framework different from other individual long-term or short-term memory works. Extensive experiments on multi-turn tasks including ALFWorld, Webshop, and PDDL across diverse models demonstrate that M-Plan outperforms multiple baseline methods in both accuracy and efficiency.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-19T16:30:42Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Ziyang Liu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jiakang Yuan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jiayuan Fan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Tao Chen</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.27634v1</id>
|
||||
<title>Standards-Guided and Dependency-Aware Code-Level Documentation Generation</title>
|
||||
<updated>2026-10-20T04:58:06Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.27634v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.27634v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Automatically generating high-quality documentation for software repositories remains challenging: existing approaches largely treat documentation as unconstrained text generation, lacking explicit quality specifications, depending heavily on long-context prompts over raw code, and offering limited support for reproducible evaluation. We argue that code-level documentation generation should instead be a standards-guided software engineering task, in which documentation is generated and assessed against explicit, reusable quality requirements derived from authoritative standards. We define code documentation as natural-language descriptions of repository functions and classes, organized by source-code path. We present CODEDOC, a standards-guided and dependency-aware framework that identifies dependencies between code entities via static call-graph and AST analysis and constructs dependency-aware structured prompts combining local code context, repository paths, and documentation criteria drawn from authoritative standards and style guides. We further introduce an interactive LLM-as-a-judge protocol that supplies criterion-aligned feedback to refine generated documentation along fine-grained dimensions from formal and practice-oriented standards. Experiments on ten open-source Python repositories show that CODEDOC produces higher-quality documentation than RepoAgent and a long-context baseline under standards-aligned evaluation, with relative improvements of 11.6%-17.0% and 21.4%-29.6%, respectively, while consuming roughly an order of magnitude fewer tokens per documentation entity than the long-context baseline. A human study confirms substantial alignment between LLM-based and human judgments (Spearman's rho = 0.72), and the relative findings hold across different judge LLMs. Code is available at https://github.com/ControllableModel/CodeDoc.</summary>
|
||||
<category term="cs.SE" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T04:58:06Z</published>
|
||||
<arxiv:primary_category term="cs.SE"/>
|
||||
<author>
|
||||
<name>Tingxu Han</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Wei Song</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Ziming Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Yuchen Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Weisong Sun</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zhenpeng Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Chunrong Fang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zhenyu Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Yang Liu</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.28385v1</id>
|
||||
<title>RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies</title>
|
||||
<updated>2026-10-20T15:34:02Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.28385v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.28385v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Head-dimension compression can reduce the memory required by the key-value (KV) cache, but existing methods often reconstruct full-dimensional representations during inference, adding significant decoding FLOPs overhead. We formalize the design considerations for converting pretrained attention into a natively reduced-dimensional form that reduces both KV-cache storage and attention computation using a calibration set. Specifically, we consider reductions that (1) apply a shared projection to queries and keys so that they remain in a common reduced feature space, (2) derive this projection jointly from query and key activations to better preserve their interaction, and (3) are compatible with the pretrained RoPE transformation. To obtain a reconstruction-free realization of this reduction, we restrict it to coordinate selection and theoretically show that RoPE compatibility then holds if and only if complete rotary pairs are retained. Building on this characterization, we introduce RACE, a post-training method that selects rotary pairs using their joint query-key activation energy. We theoretically show that this criterion minimizes a separable, calibration-dependent upper bound on post-RoPE query-key product approximation error. The resulting reductions are folded into the model weights and require neither runtime reconstruction nor specialized kernels. Across three model families and different compression levels, RACE outperforms structured pruning and remains competitive with reconstruction-based methods on language-modeling, zero-shot, and long-context tasks. At 50% KV-cache retention and 256K context, RACE reduces total inference FLOPs by 47.8%, yielding 1.79x higher throughput, 43.2% lower time-to-first-token (TTFT), and 28.1% lower allocated GPU memory in comparison to an uncompressed model.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T15:34:02Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Irene Tenison</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Abdulrahman Alabdulkareem</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Daniel Karl I. Weidele</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Nima Dehmamy</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Dan Gutfreund</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.27234v1</id>
|
||||
<title>EGI-BENCH: An Enterprise General Intelligence Benchmark Suite</title>
|
||||
<updated>2026-10-19T20:02:07Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.27234v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.27234v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, function calling, long-horizon research, and long-context software engineering. These evaluations live today in separate repositories with divergent runners, dependencies, and scoring conventions, which makes cross-benchmark comparison difficult and obscures what a single aggregate number actually represents. We release EGI-BENCH, a unified LLM agent benchmark suite that consolidates eight enterprise facing agent benchmarks (CRMARENA, HERB, PERSONABENCH, USERBENCH, MCP-UNIVERSE, MCPEVAL, BENCHFORCE, LOCOBENCH) behind a single command-line interface with two runtime-budgeted execution modes. On top of the suite we define the Enterprise General Intelligence (EGI) Score, a six-dimensional framework (M1-M6) whose buckets are populated by fine-grained sub-metrics routed from each benchmark rather than by benchmark-level averages. We describe the suite, the sub-metric routing, and the quick/deep budgets, and report results for fourteen frontier and cost-efficient LLMs spanning the GPT, Claude, and Gemini families under quick mode. Top-line EGI ranges from 39.6 (GPT-4o mini) to 65.6 (gpt-5.5), and no single model dominates every capability axis: gpt-5.5 leads task outcome, planning, and retrieval grounding; Claude Sonnet 4.5 leads tool actioning and safety/abstention; and Claude Haiku 4.5 leads engineering quality. Code is available at https://github.com/SalesforceAIResearch/egi-bench.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-19T20:02:07Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Zhiwei Liu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zhujun Lan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jielin Qiu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zixiang Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Liangwei Yang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Ming Zhu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Akshara Prabhakar</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Wenting Zhao</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>John Emmons</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jason Wu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Shafiq Rayhan Joty</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Junnan Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Shelby Heinecke</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Silvio Savarese</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Huan Wang</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.29820v1</id>
|
||||
<title>UlaCache: Unified Logical Addressing for Sparse LLM Serving</title>
|
||||
<updated>2026-10-21T14:46:11Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.29820v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.29820v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Long contexts increase the memory cost of the key-value (KV) cache in large language model (LLM) inference. Sparse attention reduces attention computation and memory traffic by selecting historical tokens, but the retained KV cache must remain available for later decoding steps. We present UlaCache, a KV cache management system built around a unified logical address space spanning GPU KV pools and optional CPU memory. Stable logical references let the inference engine allocate and select KV independently of physical placement. Common allocation and sparse-access interfaces support data-parallel (DP) and tensor-parallel (TP) attention. For distributed storage, local GPU buffers retain selected KV across decoding steps, so only missing entries require transfers. When CPU storage is enabled, Parallel Bucketed LRU (PB-LRU) manages a GPU pool shared across requests. It updates recency metadata and selects eviction victims in parallel. UlaCache transfers KV and then updates the address mappings. With speculative decoding enabled under matched CPU and GPU memory budgets, UlaCache achieves peak decoding throughput speedups of 1.16 $\times$ (DP) and 1.70 $\times$ (TP) on H200, and 1.73 $\times$ (DP) and 2.51 $\times$ (TP) on B300, compared with the state-of-the-art baseline.</summary>
|
||||
<category term="cs.DC" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-21T14:46:11Z</published>
|
||||
<arxiv:primary_category term="cs.DC"/>
|
||||
<author>
|
||||
<name>Isaac Zhu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Alan Cao</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hugh Yin</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Rocky Wang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Orion Wang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hscos Zhang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jack Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jason Zhao</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.31169v1</id>
|
||||
<title>SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents</title>
|
||||
<updated>2026-10-22T12:25:27Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.31169v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.31169v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Multi-turn LLM agents usually reuse long context prefixes, making key value (KV) caching crucial for reducing prefill latency. SSDs provide a low-cost extension to limited DRAM capacity but introduce two key challenges. Eagerly persisting all KV cache blocks incurs substantial write traffic and rapidly consumes finite SSD endurance. Meanwhile, the small number of SSDs provisioned in typical GPU servers limits aggregate reload bandwidth, making SSD-resident KV cache much slower to retrieve than that in DRAM and inflating prefill latency.
|
||||
We present \textsc{Saria} (Selective Admission and Reload for Inference Acceleration), a KV caching system that jointly manages DRAM and SSD by exploiting the session-level temporal locality of LLM agents to reduce SSD writes and accelerate cache reload. On the write path, \textsc{Saria} uses eviction and reuse history to selectively persist KV cache blocks that exhibit reuse opportunities beyond their DRAM residency, avoiding unnecessary writes for KV cache already hosted in DRAM. On the read path, \textsc{Saria} promotes selected SSD-hit KV cache blocks from GPU memory into DRAM after prefill, allowing subsequent reuse within the same session to reduce SSD read traffic while limiting DRAM contention. A transfer coordinator paces DRAM reads while SSD transfers are active, allowing both paths to make progress and efficiently share GPU ingress bandwidth.
|
||||
We implement \textsc{Saria} on LMCache MP and evaluate it using workloads from TraceLab and WEKA, covering both full-attention and hybrid-attention models. The results show that \textsc{Saria} reduces SSD writes by 57.1--58.9\% compared with popular full duplicate caching and the mean time to first token (TTFT) by 42.3--79.7\% over DRAM-only caching.</summary>
|
||||
<category term="cs.OS" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-22T12:25:27Z</published>
|
||||
<arxiv:comment>12 pages, 16 figures</arxiv:comment>
|
||||
<arxiv:primary_category term="cs.OS"/>
|
||||
<author>
|
||||
<name>Hui Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Qihui Zhou</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Xiao Yan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Shumeng Zhang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zijie Dai</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zequn Gong</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Mingcong Song</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Guoping Long</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hongjie Si</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>James Cheng</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.25640v1</id>
|
||||
<title>Think, Compress, Answer: Reinforcement Learning for Multimodal Grounding via Reasoning Compression</title>
|
||||
<updated>2026-10-19T01:34:19Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.25640v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.25640v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Extended reasoning has substantially improved multimodal large language models (MLLMs) on tasks requiring compositional and relational inference. However, multimodal grounding imposes a distinct requirement: reasoning must ultimately converge on a target-specific compact representation that supports precise localization. As reasoning trajectories grow longer, they often expand into broad scene narratives and peripheral relational details, which can dilute grounded perceptual evidence and lead to ambiguous or incorrect predictions. Motivated by the information bottleneck principle, we propose Think-Compress-Answer (TCA), a reinforcement learning framework that decomposes multimodal grounding into three phases: free-form reasoning, target-aware context compression, and answer generation conditioned on the compressed context. TCA is optimized through a multi-stage Group Relative Policy Optimization (GRPO) with progressive rewards that establish the structured output protocol, minimize reasoning-specific information while preserving target-relevant content, and directly improve localization accuracy. This staged design enables MLLMs to retain the benefits of extended reasoning while distilling it into the minimal context required for grounding. Experiments across six multimodal grounding benchmarks show that TCA attains substantial zero-shot performance gains, demonstrating the effectiveness of reasoning compression.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-19T01:34:19Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Jinbae Seo</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hyeongjun Kwon</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jiyoung Lee</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hyunsung Jang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Namkoo Ha</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jungin Park</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Kwanghoon Sohn</name>
|
||||
</author>
|
||||
</entry>
|
||||
</feed>
|
||||
@@ -0,0 +1,318 @@
|
||||
<?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/lzCoOKH2nei3auM0yuLPfHykWJM</id>
|
||||
<title>arXiv Query: search_query=&id_list=2610.25640,2610.27032,2610.27234,2610.27634,2610.28223,2610.28385,2610.28441,2610.29820,2610.31169&start=0&max_results=50</title>
|
||||
<updated>2026-10-25T14:14:40Z</updated>
|
||||
<link href="https://arxiv.org/api/query?search_query=&start=0&max_results=50&id_list=2610.25640,2610.27032,2610.27234,2610.27634,2610.28223,2610.28385,2610.28441,2610.29820,2610.31169" type="application/atom+xml"/>
|
||||
<opensearch:itemsPerPage>50</opensearch:itemsPerPage>
|
||||
<opensearch:totalResults>9</opensearch:totalResults>
|
||||
<opensearch:startIndex>0</opensearch:startIndex>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.28223v1</id>
|
||||
<title>Local Inference of LLMs on Long Contexts: A Data Systems Perspective</title>
|
||||
<updated>2026-10-20T13:56:12Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.28223v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.28223v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Large language models can solve complex problems across a variety of subjects, such as mathematics and coding. For such complex analytical tasks, models with a long context perform better. Serving models with long context is currently mostly done on powerful systems with multiple GPUs, which are too expensive for most users. An alternative is local inference, or serving a model on a user's own machine, which removes per-token costs and rate limits, besides keeping sensitive data private and giving users full control over the models they run. The common understanding, however, is that local LLMs need compression (e.g., quantization) to run on a single GPU. In this paper, we set out to study the limitations that prevent serving long-context models for single users, besides the capacity issue. Rather than characterizing a single model on one GPU, we analyze how different models behave across several GPU generations. This lets us uncover not just individual bottlenecks but broader trends in where inefficiencies arise. Our analysis confirms that, as expected, self-attention dominates the runtime for long-context inference. More unexpectedly, our evaluation reveals that the current structure of self-attention leaves key GPU resources significantly underutilized, and the newest GPU is often the least well served. Many of these findings are fundamentally data management problems, from paging the KV cache to improving cache utilization and synchronization in the GPU, which is why we bring this study to the Data Systems community. To engage this community, we present our findings as a series of experimental observations and formulate recommendations for future work.</summary>
|
||||
<category term="cs.DB" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T13:56:12Z</published>
|
||||
<arxiv:primary_category term="cs.DB"/>
|
||||
<author>
|
||||
<name>Mattias Dürrmeier</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Alberto Lerner</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.28441v1</id>
|
||||
<title>Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity</title>
|
||||
<updated>2026-10-20T16:02:12Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.28441v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.28441v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Large Language Model inference over long contexts is increasingly bottlenecked by the cost of attending to a growing Key-Value (KV) cache. Sparse attention reduces this cost by accessing only a fraction of the cache, but existing methods face a trade-off between deterministic retrieval, which captures high-scoring tokens but discards residual attention mass, and sampling, which improves coverage at the cost of higher variance. Hybrid methods combine top-$k$ retrieval with uniform sampling, yet still exhibit a substantial gap from dense attention at aggressive sparsity. We introduce \emph{Stratified Sparse Attention (SSA)}, a unified framework combining deterministic retrieval with score-guided sampling. SSA deterministically attends to high-scoring tokens while sampling the residual using proxy attention scores and correcting for their inclusion probabilities, thereby recovering attention mass beyond the retrieval set. SSA encompasses top-$k$, top-$p$, and hybrid top-$k$+sampling as special cases. Across RULER, LongBench, LOFT, and AIME, and five models ranging from 3B to 27B parameters, SSA consistently improves over the baselines at matched sparsity. On RULER-HARD with Ministral-3-8B, SSA improves accuracy over the strongest practical baseline from 81.8\% to 84.2\% at 32K and from 63.8\% to 69.3\% at 128K under $50\times$ attention sparsity, recovering 94\% and 85\% of dense-attention performance, respectively. With optimized CUDA kernels, SSA achieves up to $2.96\times$ decode speedup over FlashInfer at 1M context, demonstrating that its accuracy gains translate into practical long-context inference acceleration. We open-source our code at https://github.com/skylight-org/SSA-Paper-Code.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T16:02:12Z</published>
|
||||
<arxiv:comment>26 pages, 5 figures, 13 tables</arxiv:comment>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Sahil Joshi</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Prithvi Dixit</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Aryan Katiyar</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Agniva Chowdhury</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Joseph E. Gonzalez</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Anshumali Shrivastava</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Aditya Desai</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Kumar Krishna Agrawal</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.27032v1</id>
|
||||
<title>M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement</title>
|
||||
<updated>2026-10-19T16:30:42Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.27032v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.27032v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, particularly in environments without prior training. A key limitation of single LLMs is their difficulty in processing long contexts, which stems from weaknesses in continuous decision-making and reasoning capabilities. Although existing multi-agent systems (MAS) equipped with long-term memory bases can partially address this gap, they mostly rely on knowledge reuse or summarization, an approach that is often constrained by directly retrieval without any adaptation for the current task, while the retrieved memory may contain much history noises waiting to be preprocessed. In contrast, MAS characterized by task-specific guidance often lack long-term memory specifically designed for the guidance. To address these challenges, we propose M-Plan, a closed-loop MAS framework that integrates long-term and short-term memory. By combining global-local and past-current guidance, M-Plan enhances agents' performance on multi-turn tasks while maintaining a plug-and-play design. During task execution, agents receive task-specific milestones and insights, and are provided with local hints only when confusion arises, which form global-local guidance together. These guidance generation and utilization construct the short-term memory as well. After task completion, relevant information about the current task is optimized or reorganized before being sent to the long-term memory storage for future retrieval. The designments mentioned above build a comprehensive framework different from other individual long-term or short-term memory works. Extensive experiments on multi-turn tasks including ALFWorld, Webshop, and PDDL across diverse models demonstrate that M-Plan outperforms multiple baseline methods in both accuracy and efficiency.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-19T16:30:42Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Ziyang Liu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jiakang Yuan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jiayuan Fan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Tao Chen</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.27634v1</id>
|
||||
<title>Standards-Guided and Dependency-Aware Code-Level Documentation Generation</title>
|
||||
<updated>2026-10-20T04:58:06Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.27634v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.27634v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Automatically generating high-quality documentation for software repositories remains challenging: existing approaches largely treat documentation as unconstrained text generation, lacking explicit quality specifications, depending heavily on long-context prompts over raw code, and offering limited support for reproducible evaluation. We argue that code-level documentation generation should instead be a standards-guided software engineering task, in which documentation is generated and assessed against explicit, reusable quality requirements derived from authoritative standards. We define code documentation as natural-language descriptions of repository functions and classes, organized by source-code path. We present CODEDOC, a standards-guided and dependency-aware framework that identifies dependencies between code entities via static call-graph and AST analysis and constructs dependency-aware structured prompts combining local code context, repository paths, and documentation criteria drawn from authoritative standards and style guides. We further introduce an interactive LLM-as-a-judge protocol that supplies criterion-aligned feedback to refine generated documentation along fine-grained dimensions from formal and practice-oriented standards. Experiments on ten open-source Python repositories show that CODEDOC produces higher-quality documentation than RepoAgent and a long-context baseline under standards-aligned evaluation, with relative improvements of 11.6%-17.0% and 21.4%-29.6%, respectively, while consuming roughly an order of magnitude fewer tokens per documentation entity than the long-context baseline. A human study confirms substantial alignment between LLM-based and human judgments (Spearman's rho = 0.72), and the relative findings hold across different judge LLMs. Code is available at https://github.com/ControllableModel/CodeDoc.</summary>
|
||||
<category term="cs.SE" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T04:58:06Z</published>
|
||||
<arxiv:primary_category term="cs.SE"/>
|
||||
<author>
|
||||
<name>Tingxu Han</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Wei Song</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Ziming Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Yuchen Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Weisong Sun</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zhenpeng Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Chunrong Fang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zhenyu Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Yang Liu</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.28385v1</id>
|
||||
<title>RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies</title>
|
||||
<updated>2026-10-20T15:34:02Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.28385v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.28385v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Head-dimension compression can reduce the memory required by the key-value (KV) cache, but existing methods often reconstruct full-dimensional representations during inference, adding significant decoding FLOPs overhead. We formalize the design considerations for converting pretrained attention into a natively reduced-dimensional form that reduces both KV-cache storage and attention computation using a calibration set. Specifically, we consider reductions that (1) apply a shared projection to queries and keys so that they remain in a common reduced feature space, (2) derive this projection jointly from query and key activations to better preserve their interaction, and (3) are compatible with the pretrained RoPE transformation. To obtain a reconstruction-free realization of this reduction, we restrict it to coordinate selection and theoretically show that RoPE compatibility then holds if and only if complete rotary pairs are retained. Building on this characterization, we introduce RACE, a post-training method that selects rotary pairs using their joint query-key activation energy. We theoretically show that this criterion minimizes a separable, calibration-dependent upper bound on post-RoPE query-key product approximation error. The resulting reductions are folded into the model weights and require neither runtime reconstruction nor specialized kernels. Across three model families and different compression levels, RACE outperforms structured pruning and remains competitive with reconstruction-based methods on language-modeling, zero-shot, and long-context tasks. At 50% KV-cache retention and 256K context, RACE reduces total inference FLOPs by 47.8%, yielding 1.79x higher throughput, 43.2% lower time-to-first-token (TTFT), and 28.1% lower allocated GPU memory in comparison to an uncompressed model.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-20T15:34:02Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Irene Tenison</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Abdulrahman Alabdulkareem</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Daniel Karl I. Weidele</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Nima Dehmamy</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Dan Gutfreund</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.27234v1</id>
|
||||
<title>EGI-BENCH: An Enterprise General Intelligence Benchmark Suite</title>
|
||||
<updated>2026-10-19T20:02:07Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.27234v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.27234v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, function calling, long-horizon research, and long-context software engineering. These evaluations live today in separate repositories with divergent runners, dependencies, and scoring conventions, which makes cross-benchmark comparison difficult and obscures what a single aggregate number actually represents. We release EGI-BENCH, a unified LLM agent benchmark suite that consolidates eight enterprise facing agent benchmarks (CRMARENA, HERB, PERSONABENCH, USERBENCH, MCP-UNIVERSE, MCPEVAL, BENCHFORCE, LOCOBENCH) behind a single command-line interface with two runtime-budgeted execution modes. On top of the suite we define the Enterprise General Intelligence (EGI) Score, a six-dimensional framework (M1-M6) whose buckets are populated by fine-grained sub-metrics routed from each benchmark rather than by benchmark-level averages. We describe the suite, the sub-metric routing, and the quick/deep budgets, and report results for fourteen frontier and cost-efficient LLMs spanning the GPT, Claude, and Gemini families under quick mode. Top-line EGI ranges from 39.6 (GPT-4o mini) to 65.6 (gpt-5.5), and no single model dominates every capability axis: gpt-5.5 leads task outcome, planning, and retrieval grounding; Claude Sonnet 4.5 leads tool actioning and safety/abstention; and Claude Haiku 4.5 leads engineering quality. Code is available at https://github.com/SalesforceAIResearch/egi-bench.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-19T20:02:07Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Zhiwei Liu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zhujun Lan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jielin Qiu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zixiang Chen</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Liangwei Yang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Ming Zhu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Akshara Prabhakar</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Wenting Zhao</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>John Emmons</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jason Wu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Shafiq Rayhan Joty</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Junnan Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Shelby Heinecke</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Silvio Savarese</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Huan Wang</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.29820v1</id>
|
||||
<title>UlaCache: Unified Logical Addressing for Sparse LLM Serving</title>
|
||||
<updated>2026-10-21T14:46:11Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.29820v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.29820v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Long contexts increase the memory cost of the key-value (KV) cache in large language model (LLM) inference. Sparse attention reduces attention computation and memory traffic by selecting historical tokens, but the retained KV cache must remain available for later decoding steps. We present UlaCache, a KV cache management system built around a unified logical address space spanning GPU KV pools and optional CPU memory. Stable logical references let the inference engine allocate and select KV independently of physical placement. Common allocation and sparse-access interfaces support data-parallel (DP) and tensor-parallel (TP) attention. For distributed storage, local GPU buffers retain selected KV across decoding steps, so only missing entries require transfers. When CPU storage is enabled, Parallel Bucketed LRU (PB-LRU) manages a GPU pool shared across requests. It updates recency metadata and selects eviction victims in parallel. UlaCache transfers KV and then updates the address mappings. With speculative decoding enabled under matched CPU and GPU memory budgets, UlaCache achieves peak decoding throughput speedups of 1.16 $\times$ (DP) and 1.70 $\times$ (TP) on H200, and 1.73 $\times$ (DP) and 2.51 $\times$ (TP) on B300, compared with the state-of-the-art baseline.</summary>
|
||||
<category term="cs.DC" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-21T14:46:11Z</published>
|
||||
<arxiv:primary_category term="cs.DC"/>
|
||||
<author>
|
||||
<name>Isaac Zhu</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Alan Cao</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hugh Yin</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Rocky Wang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Orion Wang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hscos Zhang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jack Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jason Zhao</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.31169v1</id>
|
||||
<title>SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents</title>
|
||||
<updated>2026-10-22T12:25:27Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.31169v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.31169v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Multi-turn LLM agents usually reuse long context prefixes, making key value (KV) caching crucial for reducing prefill latency. SSDs provide a low-cost extension to limited DRAM capacity but introduce two key challenges. Eagerly persisting all KV cache blocks incurs substantial write traffic and rapidly consumes finite SSD endurance. Meanwhile, the small number of SSDs provisioned in typical GPU servers limits aggregate reload bandwidth, making SSD-resident KV cache much slower to retrieve than that in DRAM and inflating prefill latency.
|
||||
We present \textsc{Saria} (Selective Admission and Reload for Inference Acceleration), a KV caching system that jointly manages DRAM and SSD by exploiting the session-level temporal locality of LLM agents to reduce SSD writes and accelerate cache reload. On the write path, \textsc{Saria} uses eviction and reuse history to selectively persist KV cache blocks that exhibit reuse opportunities beyond their DRAM residency, avoiding unnecessary writes for KV cache already hosted in DRAM. On the read path, \textsc{Saria} promotes selected SSD-hit KV cache blocks from GPU memory into DRAM after prefill, allowing subsequent reuse within the same session to reduce SSD read traffic while limiting DRAM contention. A transfer coordinator paces DRAM reads while SSD transfers are active, allowing both paths to make progress and efficiently share GPU ingress bandwidth.
|
||||
We implement \textsc{Saria} on LMCache MP and evaluate it using workloads from TraceLab and WEKA, covering both full-attention and hybrid-attention models. The results show that \textsc{Saria} reduces SSD writes by 57.1--58.9\% compared with popular full duplicate caching and the mean time to first token (TTFT) by 42.3--79.7\% over DRAM-only caching.</summary>
|
||||
<category term="cs.OS" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-22T12:25:27Z</published>
|
||||
<arxiv:comment>12 pages, 16 figures</arxiv:comment>
|
||||
<arxiv:primary_category term="cs.OS"/>
|
||||
<author>
|
||||
<name>Hui Li</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Qihui Zhou</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Xiao Yan</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Shumeng Zhang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zijie Dai</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Zequn Gong</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Mingcong Song</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Guoping Long</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hongjie Si</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>James Cheng</name>
|
||||
</author>
|
||||
</entry>
|
||||
<entry>
|
||||
<id>http://arxiv.org/abs/2610.25640v1</id>
|
||||
<title>Think, Compress, Answer: Reinforcement Learning for Multimodal Grounding via Reasoning Compression</title>
|
||||
<updated>2026-10-19T01:34:19Z</updated>
|
||||
<link href="https://arxiv.org/abs/2610.25640v1" rel="alternate" type="text/html"/>
|
||||
<link href="https://arxiv.org/pdf/2610.25640v1" rel="related" type="application/pdf" title="pdf"/>
|
||||
<summary>Extended reasoning has substantially improved multimodal large language models (MLLMs) on tasks requiring compositional and relational inference. However, multimodal grounding imposes a distinct requirement: reasoning must ultimately converge on a target-specific compact representation that supports precise localization. As reasoning trajectories grow longer, they often expand into broad scene narratives and peripheral relational details, which can dilute grounded perceptual evidence and lead to ambiguous or incorrect predictions. Motivated by the information bottleneck principle, we propose Think-Compress-Answer (TCA), a reinforcement learning framework that decomposes multimodal grounding into three phases: free-form reasoning, target-aware context compression, and answer generation conditioned on the compressed context. TCA is optimized through a multi-stage Group Relative Policy Optimization (GRPO) with progressive rewards that establish the structured output protocol, minimize reasoning-specific information while preserving target-relevant content, and directly improve localization accuracy. This staged design enables MLLMs to retain the benefits of extended reasoning while distilling it into the minimal context required for grounding. Experiments across six multimodal grounding benchmarks show that TCA attains substantial zero-shot performance gains, demonstrating the effectiveness of reasoning compression.</summary>
|
||||
<category term="cs.AI" scheme="http://arxiv.org/schemas/atom"/>
|
||||
<published>2026-10-19T01:34:19Z</published>
|
||||
<arxiv:primary_category term="cs.AI"/>
|
||||
<author>
|
||||
<name>Jinbae Seo</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hyeongjun Kwon</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jiyoung Lee</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Hyunsung Jang</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Namkoo Ha</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Jungin Park</name>
|
||||
</author>
|
||||
<author>
|
||||
<name>Kwanghoon Sohn</name>
|
||||
</author>
|
||||
</entry>
|
||||
</feed>
|
||||
@@ -0,0 +1,63 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Which categories do the 9 residual records belong to, and why did /list/ miss them?
|
||||
|
||||
If a record's PRIMARY category is outside cs.CL/CV/LG, the primary-category
|
||||
month listing will not contain it -- arXiv /list/<primary>/ only enumerates
|
||||
primary-category items. That would fully explain the zero listing hits without
|
||||
impugning the record.
|
||||
|
||||
This reads the <category term="..."> and primary_category from the exact id_list
|
||||
lookup and reports the primary for each residual ID.
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
BASE = "https://export.arxiv.org/api/query"
|
||||
ATOM_NS = "{http://www.w3.org/2005/Atom}"
|
||||
ARXIV_NS = "{http://arxiv.org/schemas/atom}"
|
||||
UA = "sphinx-archive-verifier/1.0 (evidence-lane)"
|
||||
RESIDUAL = ["2610.25640", "2610.27032", "2610.27234", "2610.27634",
|
||||
"2610.28223", "2610.28385", "2610.28441", "2610.29820", "2610.31169"]
|
||||
|
||||
|
||||
def main():
|
||||
url = BASE + "?" + urllib.parse.urlencode({"id_list": ",".join(RESIDUAL),
|
||||
"max_results": 50})
|
||||
req = urllib.request.Request(url, headers={"User-Agent": UA})
|
||||
with urllib.request.urlopen(req, timeout=45) as resp:
|
||||
xml = resp.read()
|
||||
open("/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25/"
|
||||
"atom-residual-categories.xml", "wb").write(xml)
|
||||
root = ET.fromstring(xml)
|
||||
|
||||
rows = {}
|
||||
for entry in root.findall(f"{ATOM_NS}entry"):
|
||||
eid = (entry.findtext(f"{ATOM_NS}id") or "").strip()
|
||||
m = re.search(r"/abs/([0-9]{4}\.[0-9]{4,5})", eid)
|
||||
if not m:
|
||||
continue
|
||||
primary = entry.find(f"{ARXIV_NS}primary_category")
|
||||
prim = primary.get("term") if primary is not None else None
|
||||
cats = [c.get("term") for c in entry.findall(f"{ATOM_NS}category")]
|
||||
rows[m.group(1)] = {
|
||||
"primary_category": prim,
|
||||
"all_categories": cats,
|
||||
"in_sc_CL_CV_LG_primary": prim in {"cs.CL", "cs.CV", "cs.LG"},
|
||||
}
|
||||
print(f"{m.group(1)} primary={prim} all={cats}", flush=True)
|
||||
|
||||
json.dump(rows, open("/root/.hermes/wizard-outputs/sphinx/"
|
||||
"arxiv-digest-verified-2026-10-25/RESIDUAL_CATEGORIES.json",
|
||||
"w", encoding="utf-8"), indent=1, sort_keys=True)
|
||||
outside = [k for k, v in rows.items() if not v["in_sc_CL_CV_LG_primary"]]
|
||||
print(f"\nprimary outside cs.CL/CV/LG: {len(outside)}/{len(rows)} -> {outside}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,107 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Why did the 9 residual IDs fail the API window filter?
|
||||
|
||||
Hypothesis: the Atom `submittedDate` filter is coarse/differently-indexed than the
|
||||
corpus's own `published` field, not that the records are absent. Test it by
|
||||
querying each residual ID directly with id_list= (an exact lookup that bypasses
|
||||
all date filtering), and by re-querying the same window with a WIDER date range.
|
||||
|
||||
If id_list returns the record with a real title and a date inside the window,
|
||||
the record exists and the earlier miss was a query artifact -- which must be
|
||||
reported as such, not papered over.
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
BASE = "https://export.arxiv.org/api/query"
|
||||
ATOM_NS = "{http://www.w3.org/2005/Atom}"
|
||||
UA = "sphinx-archive-verifier/1.0 (evidence-lane)"
|
||||
OUTDIR = "/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25"
|
||||
|
||||
RESIDUAL = ["2610.25640", "2610.27032", "2610.27234", "2610.27634",
|
||||
"2610.28223", "2610.28385", "2610.28441", "2610.29820", "2610.31169"]
|
||||
|
||||
|
||||
def fetch(url):
|
||||
req = urllib.request.Request(url, headers={"User-Agent": UA})
|
||||
with urllib.request.urlopen(req, timeout=45) as resp:
|
||||
return resp.read()
|
||||
|
||||
|
||||
def parse(xml_bytes):
|
||||
root = ET.fromstring(xml_bytes)
|
||||
out = {}
|
||||
for entry in root.findall(f"{ATOM_NS}entry"):
|
||||
eid = (entry.findtext(f"{ATOM_NS}id") or "").strip()
|
||||
m = re.search(r"/abs/([0-9]{4}\.[0-9]{4,5})(v\d+)?$", eid)
|
||||
if not m:
|
||||
continue
|
||||
title = " ".join((entry.findtext(f"{ATOM_NS}title") or "").split())
|
||||
out[m.group(1)] = {
|
||||
"title": title,
|
||||
"published": (entry.findtext(f"{ATOM_NS}published") or "").strip(),
|
||||
"updated": (entry.findtext(f"{ATOM_NS}updated") or "").strip(),
|
||||
}
|
||||
return out
|
||||
|
||||
|
||||
def main():
|
||||
results = {}
|
||||
|
||||
# 1) exact lookup, no date filter at all
|
||||
url = BASE + "?" + urllib.parse.urlencode(
|
||||
{"id_list": ",".join(RESIDUAL), "max_results": 50})
|
||||
xml = fetch(url)
|
||||
open(f"{OUTDIR}/atom-residual-idlist.xml", "wb").write(xml)
|
||||
exact = parse(xml)
|
||||
results["id_list_lookup"] = {k: exact.get(k) for k in RESIDUAL}
|
||||
print(f"[id_list] returned {len(exact)} of {len(RESIDUAL)}", flush=True)
|
||||
for k in RESIDUAL:
|
||||
row = exact.get(k)
|
||||
print(f" {k} {'FOUND ' + row['published'][:10] if row else 'ABSENT'}", flush=True)
|
||||
time.sleep(2)
|
||||
|
||||
# 2) wide window per category -- does the record appear when the date
|
||||
# filter is relaxed? If yes, the miss was a filter artifact.
|
||||
wide = {}
|
||||
for cat in ["cs.CL", "cs.CV", "cs.LG"]:
|
||||
query = f"cat:{cat} AND submittedDate:[202610180000 TO 202610260000]"
|
||||
url = BASE + "?" + urllib.parse.urlencode(
|
||||
{"search_query": query, "start": 0, "max_results": 1000,
|
||||
"sortBy": "submittedDate", "sortOrder": "descending"})
|
||||
wide.update(parse(fetch(url)))
|
||||
time.sleep(2)
|
||||
results["wide_window_present"] = {k: (k in wide) for k in RESIDUAL}
|
||||
present_wide = [k for k in RESIDUAL if k in wide]
|
||||
print(f"[wide window] {len(present_wide)}/{len(RESIDUAL)} present", flush=True)
|
||||
|
||||
# 3) the decisive question: are the residual records' own published dates
|
||||
# inside the 7-day window the digest claims?
|
||||
in_window = {}
|
||||
for k in RESIDUAL:
|
||||
row = exact.get(k)
|
||||
if not row:
|
||||
in_window[k] = None
|
||||
continue
|
||||
pub = row["published"][:10]
|
||||
in_window[k] = {"published": pub, "in_2026-10-19..25": "2026-10-19" <= pub <= "2026-10-25"}
|
||||
results["date_check"] = in_window
|
||||
|
||||
json.dump(results, open(f"{OUTDIR}/RESIDUAL_DIAGNOSIS.json", "w", encoding="utf-8"),
|
||||
indent=1, sort_keys=True)
|
||||
print("\n=== DIAGNOSIS ===")
|
||||
for k in RESIDUAL:
|
||||
row = exact.get(k)
|
||||
d = in_window.get(k) or {}
|
||||
print(f"{k} exists_via_id_list={bool(row)} pub={d.get('published')} "
|
||||
f"in_window={d.get('in_2026-10-19..25')} wide={results['wide_window_present'][k]}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,88 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Adversarial probe: do the 9 unverified corpus IDs actually exist on arXiv?
|
||||
|
||||
Reads the IDs straight out of the corpus JSONL (no retyping), then fetches each
|
||||
paper's abs page and classifies it:
|
||||
EXISTS -> HTTP 200 and the page contains the arXiv abstract anchor
|
||||
NOT_FOUND -> HTTP 404 / "not recognized"
|
||||
OTHER -> anything else (recorded verbatim)
|
||||
|
||||
This is the real test of the digest's integrity: a fabricated ID cannot
|
||||
produce a live abs page.
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
|
||||
CORPUS = "/root/.hermes/wizard-outputs/sphinx/arxiv_recent_harvest/merged_all.jsonl"
|
||||
OUT = "/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25/ABS_PROBE.json"
|
||||
UA = "sphinx-archive-verifier/1.0 (evidence-lane)"
|
||||
|
||||
|
||||
def main():
|
||||
missing = json.load(open(
|
||||
"/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25/VERIFICATION.json"
|
||||
))["unverified"]
|
||||
ids = [r["id"] for r in missing]
|
||||
|
||||
corpus_titles = {}
|
||||
for line in open(CORPUS, encoding="utf-8"):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
rec = json.loads(line)
|
||||
m = re.search(r"/abs/([0-9]{4}\.[0-9]{4,5})", rec.get("id", ""))
|
||||
if m:
|
||||
corpus_titles[m.group(1)] = rec.get("title", "")
|
||||
|
||||
rows = []
|
||||
for bid in ids:
|
||||
url = f"https://arxiv.org/abs/{bid}"
|
||||
status, note, page_title = None, "", ""
|
||||
try:
|
||||
req = urllib.request.Request(url, headers={"User-Agent": UA})
|
||||
with urllib.request.urlopen(req, timeout=45) as resp:
|
||||
status = resp.status
|
||||
body = resp.read().decode("utf-8", "replace")
|
||||
m = re.search(r"<title>(.*?)</title>", body, re.S)
|
||||
page_title = " ".join(m.group(1).split()) if m else ""
|
||||
if "not recognized" in body.lower() or "no article found" in body.lower():
|
||||
note = "page says not found"
|
||||
else:
|
||||
note = "live abstract page"
|
||||
except urllib.error.HTTPError as exc:
|
||||
status = exc.code
|
||||
note = f"HTTPError {exc.code}"
|
||||
except Exception as exc: # noqa: BLE001
|
||||
note = f"error: {exc}"
|
||||
rows.append({
|
||||
"id": bid,
|
||||
"url": url,
|
||||
"corpus_title": corpus_titles.get(bid, ""),
|
||||
"http_status": status,
|
||||
"page_title": page_title,
|
||||
"note": note,
|
||||
"exists": status == 200 and "not recognized" not in note,
|
||||
})
|
||||
print(f"{bid} status={status} {note} | {page_title[:70]}", flush=True)
|
||||
time.sleep(1.0)
|
||||
|
||||
exists = sum(1 for r in rows if r["exists"])
|
||||
out = {
|
||||
"probe": "per-ID abs-page existence check",
|
||||
"run_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
"tested": len(rows),
|
||||
"exists": exists,
|
||||
"not_found": len(rows) - exists,
|
||||
"rows": rows,
|
||||
}
|
||||
json.dump(out, open(OUT, "w", encoding="utf-8"), indent=1, sort_keys=True)
|
||||
print(f"\nexists={exists} not_found={len(rows) - exists}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,76 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Third-source probe: do the 9 residual IDs appear in a live /list/ page?
|
||||
|
||||
The /recent page shows only the day's newest items, so it can never cover a
|
||||
7-day window. arXiv's listing system also serves /list/<cat>/<YYMM> (a full
|
||||
month) and ?skip=N&show=M (pagination). This probe searches those pages for the
|
||||
residual IDs.
|
||||
|
||||
A hit here is a genuinely independent corroboration: a different service,
|
||||
different document, no shared code path with the Atom API.
|
||||
"""
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
|
||||
UA = "sphinx-archive-verifier/1.0 (evidence-lane)"
|
||||
OUTDIR = "/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25"
|
||||
RESIDUAL = ["2610.25640", "2610.27032", "2610.27234", "2610.27634",
|
||||
"2610.28223", "2610.28385", "2610.28441", "2610.29820", "2610.31169"]
|
||||
|
||||
|
||||
def fetch(url):
|
||||
req = urllib.request.Request(url, headers={"User-Agent": UA})
|
||||
with urllib.request.urlopen(req, timeout=60) as resp:
|
||||
return resp.read()
|
||||
|
||||
|
||||
def main():
|
||||
want = set(RESIDUAL)
|
||||
hits, pages = {}, []
|
||||
cats = ["cs.CL", "cs.CV", "cs.LG"]
|
||||
# The /list/<cat>/<YYMM> form 404s; the live scheme is /list/<cat>/<YYYY-MM>
|
||||
# (measured: 2610 -> 404, 2026-10 -> 200).
|
||||
months = ["2026-10"]
|
||||
|
||||
for cat in cats:
|
||||
for month in months:
|
||||
for url in (
|
||||
f"https://arxiv.org/list/{cat}/{month}",
|
||||
f"https://arxiv.org/list/{cat}/{month}?skip=0&show=2000",
|
||||
):
|
||||
try:
|
||||
html = fetch(url).decode("utf-8", "replace")
|
||||
except Exception as exc: # noqa: BLE001
|
||||
pages.append({"url": url, "error": str(exc)})
|
||||
print(f"[list] {url} -> ERROR {exc}", flush=True)
|
||||
continue
|
||||
found = set(re.findall(r"/abs/([0-9]{4}\.[0-9]{4,5})", html))
|
||||
overlap = want & found
|
||||
for bid in overlap:
|
||||
hits.setdefault(bid, []).append(url)
|
||||
pages.append({"url": url, "bytes": len(html), "abs_ids": len(found),
|
||||
"residual_hits": sorted(overlap)})
|
||||
print(f"[list] {url} -> {len(found)} ids, residual hits: {sorted(overlap)}",
|
||||
flush=True)
|
||||
time.sleep(1.5)
|
||||
|
||||
out = {
|
||||
"probe": "month-listing / pagination search for residual IDs",
|
||||
"run_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
"residual": RESIDUAL,
|
||||
"hits": hits,
|
||||
"corroborated": sorted(hits),
|
||||
"uncorroborated": sorted(want - set(hits)),
|
||||
"pages": pages,
|
||||
}
|
||||
json.dump(out, open(f"{OUTDIR}/LISTING_PROBE.json", "w", encoding="utf-8"),
|
||||
indent=1, sort_keys=True)
|
||||
print(f"\ncorroborated by listing pages: {len(hits)}/{len(want)}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,256 @@
|
||||
#!/usr/bin/env python3
|
||||
"""sphinx archive-lane: independent verification of the arXiv recent-digest claims.
|
||||
|
||||
WHAT WAS WRONG
|
||||
---------------
|
||||
The digest's own verifier (arxiv_recent_harvest/verify-2026-10-25.json) recorded
|
||||
"claims": 0 and an empty "present" map for every category -- that verification
|
||||
was never actually executed.
|
||||
|
||||
WHAT THIS DOES
|
||||
--------------
|
||||
Verifies every record the digest claims (the 1210-record 7-day window) against
|
||||
THREE independent live sources:
|
||||
|
||||
S1 Atom API, exact id_list lookup -- no date filter, no ranking, no cap;
|
||||
the record either is or is not there.
|
||||
S2 Atom API, windowed search query -- the digest's own construction method,
|
||||
a consistency check (not independent).
|
||||
S3 HTML listing page -- a different service and document;
|
||||
/list/<cat>/recent carries /abs/<id>
|
||||
anchors for the day's newest items.
|
||||
|
||||
THE MEASURED TRAP (found by running this, then fixed)
|
||||
----------------------------------------------------
|
||||
An early version queried each category with max_results=400 and a
|
||||
submittedDate filter. That silently missed 1060 of 1210 records. Diagnosis:
|
||||
|
||||
* A cap truncates silently; pagination is mandatory.
|
||||
* The Atom `submittedDate` filter is NOT the record's `published` date. Nine
|
||||
in-window records (e.g. 2610.25640, published 2026-10-19) were absent from
|
||||
the filtered search yet present under an exact id_list lookup.
|
||||
* Those nine have PRIMARY categories outside cs.CL/CV/LG (cs.AI, cs.DB, cs.SE,
|
||||
cs.DC, cs.OS), so a cat:cs.CL|CV|LG filter legitimately excludes them. They
|
||||
are cross-lists into the digest's categories, which is why the corpus has
|
||||
them at all.
|
||||
|
||||
Therefore S1 (exact id_list) is the authoritative existence check, and the
|
||||
residual search-misses are reported as query artifacts, not as corpus errors.
|
||||
|
||||
Nothing here is simulated. Every field is read from a real HTTP response; a
|
||||
failed fetch is recorded as an error and fails the run.
|
||||
"""
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
ATOM_NS = "{http://www.w3.org/2005/Atom}"
|
||||
ARXIV_NS = "{http://arxiv.org/schemas/atom}"
|
||||
BASE = "https://export.arxiv.org/api/query"
|
||||
LIST_BASE = "https://arxiv.org/list/{cat}/recent"
|
||||
UA = "sphinx-archive-verifier/1.0 (evidence-lane; contact: sphinx@agents.invalid)"
|
||||
|
||||
DIGEST = "/root/.hermes/wizard-outputs/sphinx/arxiv_recent_harvest/arxiv-recent-digest-2026-10-25.md"
|
||||
CORPUS = "/root/.hermes/wizard-outputs/sphinx/arxiv_recent_harvest/merged_all.jsonl"
|
||||
OUTDIR = "/root/.hermes/wizard-outputs/sphinx/arxiv-digest-verified-2026-10-25"
|
||||
CATEGORIES = ["cs.CL", "cs.CV", "cs.LG"]
|
||||
CUTOFF = "2026-10-19"
|
||||
IDLIST_CHUNK = 120
|
||||
|
||||
|
||||
def fetch(url, tries=4, pause=3.0):
|
||||
last = None
|
||||
for attempt in range(tries):
|
||||
try:
|
||||
req = urllib.request.Request(url, headers={"User-Agent": UA})
|
||||
with urllib.request.urlopen(req, timeout=60) as resp:
|
||||
body = resp.read()
|
||||
if not body:
|
||||
raise RuntimeError("empty body")
|
||||
return body
|
||||
except Exception as exc: # noqa: BLE001
|
||||
last = exc
|
||||
if attempt < tries - 1:
|
||||
time.sleep(pause * (attempt + 1))
|
||||
raise RuntimeError(f"fetch failed: {url}: {last}")
|
||||
|
||||
|
||||
def parse_atom(xml_bytes):
|
||||
root = ET.fromstring(xml_bytes)
|
||||
out = {}
|
||||
for entry in root.findall(f"{ATOM_NS}entry"):
|
||||
eid = (entry.findtext(f"{ATOM_NS}id") or "").strip()
|
||||
m = re.search(r"/abs/([0-9]{4}\.[0-9]{4,5})(v\d+)?$", eid)
|
||||
if not m:
|
||||
continue
|
||||
title = " ".join((entry.findtext(f"{ATOM_NS}title") or "").split())
|
||||
primary = entry.find(f"{ARXIV_NS}primary_category")
|
||||
out[m.group(1)] = {
|
||||
"title": title,
|
||||
"title_sha256": hashlib.sha256(title.encode("utf-8")).hexdigest(),
|
||||
"published": (entry.findtext(f"{ATOM_NS}published") or "").strip(),
|
||||
"updated": (entry.findtext(f"{ATOM_NS}updated") or "").strip(),
|
||||
"primary_category": primary.get("term") if primary is not None else None,
|
||||
}
|
||||
return out
|
||||
|
||||
|
||||
def load_corpus():
|
||||
recs = {}
|
||||
with open(CORPUS, encoding="utf-8") as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
rec = json.loads(line)
|
||||
m = re.search(r"/abs/([0-9]{4}\.[0-9]{4,5})", rec.get("id", ""))
|
||||
if m:
|
||||
recs[m.group(1)] = rec
|
||||
return recs
|
||||
|
||||
|
||||
def main():
|
||||
os.makedirs(OUTDIR, exist_ok=True)
|
||||
errors = []
|
||||
|
||||
corpus = load_corpus()
|
||||
window = {b: r for b, r in corpus.items()
|
||||
if (r.get("published") or "")[:10] >= CUTOFF}
|
||||
print(f"[load] corpus={len(corpus)} in-window(>={CUTOFF})={len(window)}", flush=True)
|
||||
|
||||
ids = sorted(window)
|
||||
|
||||
# ---- S1: authoritative exact lookup, no date filter -------------------
|
||||
s1 = {}
|
||||
for i in range(0, len(ids), IDLIST_CHUNK):
|
||||
chunk = ids[i:i + IDLIST_CHUNK]
|
||||
url = BASE + "?" + urllib.parse.urlencode(
|
||||
{"id_list": ",".join(chunk), "max_results": len(chunk) + 10})
|
||||
try:
|
||||
s1.update(parse_atom(fetch(url)))
|
||||
except Exception as exc: # noqa: BLE001
|
||||
errors.append(f"S1 chunk {i}: {exc}")
|
||||
if i % 600 == 0:
|
||||
print(f"[S1] {i}/{len(ids)} resolved={len(s1)}", flush=True)
|
||||
time.sleep(0.8)
|
||||
print(f"[S1] exact id_list resolved {len(s1)} of {len(ids)}", flush=True)
|
||||
|
||||
# ---- S2: windowed search (digest's own method) -------------------------
|
||||
s2 = {}
|
||||
for cat in CATEGORIES:
|
||||
start, page_size = 0, 200
|
||||
while True:
|
||||
query = f"cat:{cat} AND submittedDate:[202610190000 TO 202610260000]"
|
||||
url = BASE + "?" + urllib.parse.urlencode(
|
||||
{"search_query": query, "start": start, "max_results": page_size,
|
||||
"sortBy": "submittedDate", "sortOrder": "descending"})
|
||||
try:
|
||||
page = parse_atom(fetch(url))
|
||||
except Exception as exc: # noqa: BLE001
|
||||
errors.append(f"S2 {cat}@{start}: {exc}")
|
||||
break
|
||||
if not page:
|
||||
break
|
||||
s2.update(page)
|
||||
if len(page) < page_size:
|
||||
break
|
||||
start += page_size
|
||||
if start > 6000:
|
||||
break
|
||||
time.sleep(0.8)
|
||||
print(f"[S2] {cat}: {len(s2)} total", flush=True)
|
||||
|
||||
# ---- S3: live HTML listing pages --------------------------------------
|
||||
s3, listing_hashes = set(), {}
|
||||
for cat in CATEGORIES:
|
||||
url = LIST_BASE.format(cat=cat)
|
||||
try:
|
||||
html = fetch(url)
|
||||
except Exception as exc: # noqa: BLE001
|
||||
errors.append(f"S3 {cat}: {exc}")
|
||||
continue
|
||||
with open(os.path.join(OUTDIR, f"listing-{cat}.html"), "wb") as fh:
|
||||
fh.write(html)
|
||||
found = set(re.findall(r"/abs/([0-9]{4}\.[0-9]{4,5})", html.decode("utf-8", "replace")))
|
||||
s3 |= found
|
||||
listing_hashes[cat] = {
|
||||
"url": url, "sha256": hashlib.sha256(html).hexdigest(),
|
||||
"bytes": len(html), "abs_ids_seen": len(found),
|
||||
}
|
||||
print(f"[S3] {cat}: {len(found)} /abs ids", flush=True)
|
||||
time.sleep(1.0)
|
||||
|
||||
# ---- classify every claim ---------------------------------------------
|
||||
rows = []
|
||||
for bid in ids:
|
||||
meta = s1.get(bid)
|
||||
corpus_title = (window[bid].get("title") or "").strip()
|
||||
corpus_hash = hashlib.sha256(corpus_title.encode("utf-8")).hexdigest()
|
||||
in_s2, in_s3 = bid in s2, bid in s3
|
||||
rows.append({
|
||||
"id": bid,
|
||||
"published": meta["published"] if meta else (window[bid].get("published") or ""),
|
||||
"title": meta["title"] if meta else corpus_title,
|
||||
"primary_category": meta["primary_category"] if meta else None,
|
||||
"title_sha256": meta["title_sha256"] if meta else corpus_hash,
|
||||
"title_matches_corpus": (meta["title_sha256"] == corpus_hash) if meta else None,
|
||||
"s1_exact_lookup": bool(meta),
|
||||
"s2_windowed_search": in_s2,
|
||||
"s3_html_listing": in_s3,
|
||||
"sources_agreeing": int(bool(meta)) + int(in_s2) + int(in_s3),
|
||||
})
|
||||
|
||||
verified = [r for r in rows if r["s1_exact_lookup"]]
|
||||
failed = [r for r in rows if not r["s1_exact_lookup"]]
|
||||
title_mismatch = [r for r in verified if r["title_matches_corpus"] is False]
|
||||
both_s2_s3 = [r for r in rows if r["s2_windowed_search"] and r["s3_html_listing"]]
|
||||
|
||||
result = {
|
||||
"verifier": "sphinx/arxiv-digest-verifier v2",
|
||||
"run_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
"digest_under_test": DIGEST,
|
||||
"digest_sha256": hashlib.sha256(open(DIGEST, "rb").read()).hexdigest(),
|
||||
"cutoff": CUTOFF,
|
||||
"window_size": len(rows),
|
||||
"sources": {
|
||||
"S1": "Atom API exact id_list lookup (authoritative existence check)",
|
||||
"S2": "Atom API windowed cat+submittedDate search (consistency)",
|
||||
"S3": "HTML /list/<cat>/recent page (independent service, bounded)",
|
||||
},
|
||||
"counts": {
|
||||
"claims": len(rows),
|
||||
"verified_exact_lookup": len(verified),
|
||||
"unverified": len(failed),
|
||||
"title_mismatch": len(title_mismatch),
|
||||
"present_in_s2_and_s3": len(both_s2_s3),
|
||||
},
|
||||
"listing_hashes": listing_hashes,
|
||||
"rows": rows,
|
||||
"unverified_ids": [r["id"] for r in failed],
|
||||
"title_mismatch_ids": [r["id"] for r in title_mismatch],
|
||||
"errors": errors,
|
||||
}
|
||||
out_path = os.path.join(OUTDIR, "VERIFICATION.json")
|
||||
with open(out_path, "w", encoding="utf-8") as fh:
|
||||
json.dump(result, fh, indent=1, sort_keys=True)
|
||||
|
||||
print("\n=== RESULT ===")
|
||||
print(f"claims : {len(rows)}")
|
||||
print(f"verified (S1 exact) : {len(verified)}")
|
||||
print(f"unverified : {len(failed)}")
|
||||
print(f"title mismatches : {len(title_mismatch)}")
|
||||
print(f"present in S2 and S3 : {len(both_s2_s3)}")
|
||||
print(f"errors : {len(errors)}")
|
||||
if failed:
|
||||
print("FAILED IDS:", [r["id"] for r in failed][:25])
|
||||
return 0 if not failed and not errors else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in new issue
Block a user