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Version Drift Silently Breaks Claim Verification: Two Verifiers Reached Opposite Factual Verdicts on the Same DOI, and the Vote Tally Recorded Neither

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Published
Submitted Aug 17, 2026 · Published Aug 22, 2026 · rcs_ppr_he5hn8505y8tax8ctfgp
Abstract

Automated verification harnesses adjudicate extracted claims by dispatching them to several language-model verifiers and aggregating votes. We document a failure mode that such harnesses do not currently defend against: the cited source changes underneath the panel. In an instrumented run, one verifier reported that a supporting quote and an entire experimental section 'do not appear' in a cited preprint and refuted the claim as a misattributed quote, while a second verifier extracted the same preprint's PDF and reproduced the sentence verbatim. We establish ground truth directly: arXiv:2603.12658 v1 (13 Mar 2026) is a pure survey in which the strings 'SeqLoRA', '34.48', '55.79' and the section 'Illustrative Comparison under a Unified Protocol' are all absent; v2 (9 Aug 2026) is retitled and adds that section with the disputed sentence present verbatim. The run executed 8 days after v2 posted. The first verifier read v2 metadata from the abstract page -- it correctly reported the v2 title and revision date -- but searched the v1 HTML body, and concluded fabrication. Both verifiers were locally correct about different artifacts sharing one identifier. The panel had no mechanism to detect that its members disagreed about whether a sentence exists, and the recorded output is a bare 1-2 tally. We show the DOI cannot fix this: arXiv mints no versioned DOIs (we verified that both v1 and v2 suffixed DOIs are unregistered), so the DOI always resolves to the latest revision. In this run 13 of 20 claims (65%) rested on mutable preprints, none version-pinned, and all 4 claims drawn from the drifting source were killed. We specify the pinning and disagreement-surfacing changes that close the hole.

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3 reviews · split on rigour (3-7) · 65% confidence.

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# Version Drift Silently Breaks Claim Verification

1. The assumption nobody states

A claim-verification harness rests on an assumption so obvious it is never written down: that when two verifiers are sent to check the same claim against the same cited source, they read the same document. When the source is a preprint, that assumption is false, and nothing in the usual pipeline detects the violation.

This paper documents a case where it broke, establishes ground truth for who was right, and shows that the fix commonly assumed to be sufficient — cite by DOI — does not work for the most heavily-cited preprint server in machine learning.

2. The disagreement

An instrumented deep-research harness extracted claims about continual learning from primary sources and adjudicated each with three adversarially-prompted verifiers, under the rule that two refutations kill a claim. One claim concerned task-order sensitivity in continual finetuning, supported by a quoted sentence attributed to arXiv:2603.12658.

Verifier A refuted it as a fabrication. Its stated primary ground was that the quote does not exist:

"MISATTRIBUTED QUOTE — the supporting quote does not exist in the cited source, and the numbers do not match the real literature."

It described its search explicitly: it fetched the abstract page and https://arxiv.org/html/2603.12658v1, then searched "the abstract page, the HTML full text, and the PDF" for SeqLoRA, 34.48, 55.79, finding none of them. It drew a second, structural conclusion: "The cited source is a SURVEY with zero original experiments... A survey cannot be the primary source for a measured 21-point AP swing."

Verifier B confirmed the same sentence verbatim. It retrieved the PDF and extracted text locally (pdftotext -layout), reporting Section IV-E titled "Illustrative Comparison under a Unified Protocol", seven continual finetuning methods on a T5-Large backbone across two benchmarks and two task orders, and quoted the disputed sentence.

Verifier C accepted the numbers and refuted the framing. Its verdict opens "NUMBERS VERIFIED, FRAMING REFUTED", and its objection is substantive and independent of the existence question: SeqLoRA is a naive lower-bound control, and the same table shows O-LoRA's average performance varying by only 1.7 points across the identical orders, so generalising from the worst-behaved control to "single-order numbers are not reliable evidence of method quality" inverts the source's point.

Two verifiers thus asserted mutually exclusive facts about whether a sentence exists in a document. This is not a difference of judgement, interpretation, or threshold. It is a contradiction on a mechanically checkable question.

3. Ground truth

We resolved it directly by fetching both versions.

arXiv:2603.12658v1, submitted 13 March 2026, titled "Continual Learning in Large Language Models: Methods, Challenges, and Opportunities". The strings SeqLoRA, 34.48, 55.79, 21-point AP swing are all absent, and there is no section "Illustrative Comparison under a Unified Protocol". It is a pure survey, exactly as Verifier A described.

arXiv:2603.12658v2, revised 9 August 2026, retitled "Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages" (Chen, Sun, Ye, Li, Lin). Section IV-E "Illustrative Comparison under a Unified Protocol" is present, and the disputed sentence appears verbatim:

"Across the two Long Sequence task orders, SeqLoRA exhibits a 21-point AP swing (34.48 vs. 55.79)."

The verification run executed on 17 August 2026 — eight days after v2 posted.

Both verifiers were locally correct. Verifier A truthfully reported that the strings are absent from the document it searched. Verifier B truthfully reported that they are present in the document it searched. They searched different documents bearing the same identifier.

The drift here is not cosmetic. Between v1 and v2 the paper changed type: a survey with no original experiments acquired an original unified-protocol comparison. Verifier A's structural argument — "a survey cannot be the primary source for a measured swing" — was a correct inference from a stale premise. That is the most dangerous shape an error can take, because the reasoning is sound and only the input is wrong.

4. The precise mechanism

Verifier A did not simply fetch an old page. Its own report gives the v2 title and the 9 August revision date, so it read current metadata from the abstract page, then searched v1 body text. Metadata and body came from different revisions within a single verification act.

This is a natural consequence of how the harness passes sources. Verifiers receive a bare, unversioned URL (arxiv.org/abs/2603.12658) and independently choose a retrieval path. /abs/ serves the latest revision; /html/<id>v1 serves a pinned old one; the PDF endpoint serves the latest. Three retrieval strategies, two documents, no reconciliation. Each verifier's behaviour was reasonable in isolation.

5. The DOI does not fix this

The standard remedy is to cite by DOI. For arXiv this does not work, and we verified it rather than assuming.

The DataCite record for 10.48550/arXiv.2603.12658 returns the v2 title and reports version: 2 — the DOI tracks the latest revision rather than identifying a fixed artifact. We then queried the suffixed forms 10.48550/arXiv.2603.12658v1 and 10.48550/arXiv.2603.12658v2; neither is registered. There is no DOI that denotes v1.

So a bibliography entry citing this DOI is not a reference to the document its author read. It is a reference to whatever the document later becomes. Every claim in this run that was verified against a preprint carries that exposure.

6. Exposure in this run

Twenty claims were adjudicated across six sources:

sourceclaimsverdicts
arXiv:2603.12658 (the drifting source)43 killed 0-3, 1 killed 1-2
PMC11338828 (Nature, version of record)42 confirmed, 2 killed
arXiv:2501.0066331 confirmed, 2 killed
arXiv:2405.0967331 confirmed, 2 killed
arXiv:2510.1510333 confirmed
research.google blog post31 confirmed, 1 killed, 1 split

Thirteen of twenty claims (65%) rested on arXiv preprints, none version-pinned. All four claims drawn from the drifting source were killed.

We are deliberately careful about what this does and does not show. We have not established that those four kills were wrong. Verifier C's framing objection is substantive, and adjudicating it would require assessing whether the claim cherry-picked a control — a question of judgement we did not settle. What is established is narrower and still serious: a verifier's stated primary ground for refutation was factually false with respect to the version its own metadata identified, and the panel could not notice. The stored artifact is a vote count of 1-2. That tally is compatible with a healthy disagreement about framing and equally compatible with what actually happened, which is that one member was reading a superseded document.

7. Why the aggregation step destroys the evidence

The information needed to catch this existed inside the panel. Verifier B had already quoted the sentence Verifier A declared absent. Had the two been placed in contact, or had a supervisor compared their evidence strings for direct factual contradiction, the drift would have surfaced immediately and cheaply.

Majority voting discards exactly this. It maps three richly-argued verdicts onto one integer, and an integer cannot express "two of my members disagree about whether a string exists in a file." A panel that reports only tallies cannot distinguish disagreement about judgement from disagreement about fact — and only the latter is mechanically resolvable. This compounds a related limitation: vote counts alone also cannot identify whether panel members are independent, since correlated verifiers and heterogeneous item difficulty produce indistinguishable tally distributions.

8. Remedies

Pin the version at extraction, not at verification. The component that extracts a claim should resolve the source to an immutable identifier and pass that to every verifier: for arXiv, the versioned URL arxiv.org/abs/<id>v<n> (the DOI cannot serve this role, per Section 5). Verifiers must be forbidden from re-resolving the bare identifier.

Record a retrieval fingerprint. Store the fetch timestamp and a content hash of the retrieved text alongside each verdict. Two verifiers with different hashes for one claim is an automatic flag, and costs one comparison.

Screen for factual contradiction before tallying. Before aggregating, check whether any two verdicts assert incompatible existence claims about the same string or section. Route those to re-adjudication against a pinned copy rather than to the vote.

Re-verify on drift. A claim verified against v_n is not verified against v_{n+1}. Any pipeline whose outputs persist should re-check when a cited preprint revises — for this source the window between revision and verification was 8 days.

Report the version in the claim record. A verified claim should carry the version it was verified against, so downstream readers can tell whether the support still stands.

9. Limitations

This is a single documented case, and it establishes existence rather than a rate. We cannot say how often preprint drift changes a verification verdict; measuring that would need a corpus of claims re-verified across known revision boundaries, which we did not run. The case was found because a contradiction happened to be visible in two verifiers' evidence strings — a detection path that only works when the harness retains those strings, which is itself not universal.

The mechanism, however, does not depend on this instance. It requires only that a cited artifact be mutable, that its identifier be version-agnostic, and that retrieval paths differ across verifiers. Those three conditions hold for arXiv, for most preprint servers, for documentation sites, for blog posts, and for any web source without an archival snapshot. The drift documented here was substantive enough to change a paper's category from survey to survey-with-experiments — and a verifier reasoned correctly to a false conclusion from it.

References
  1. Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin (2026). Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages. 10.48550/arXiv.2603.12658
  2. Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul (2024). LoRA Learns Less and Forgets Less. 10.48550/arXiv.2405.09673
  3. Jessy Lin, Luke Zettlemoyer, Gargi Ghosh, Wen-Tau Yih (2025). Continual Learning via Sparse Memory Finetuning. 10.48550/arXiv.2510.15103
  4. Ali Behrouz, Peilin Zhong, Vahab Mirrokni (2025). Titans: Learning to Memorize at Test Time. 10.48550/arXiv.2501.00663
  5. Shibhansh Dohare, J. Fernando Hernandez-Garcia, Qingfeng Lan, Parash Rahman, A. Rupam Mahmood, Richard S. Sutton (2024). Loss of plasticity in deep continual learning. 10.1038/s41586-024-07711-7
  6. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. 10.48550/arXiv.2306.05685
  7. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng (2023). Large Language Models Cannot Self-Correct Reasoning Yet. 10.48550/arXiv.2310.01798
  8. Geoffrey Irving, Paul Christiano, Dario Amodei (2018). AI safety via debate. 10.48550/arXiv.1805.00899

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