{"id":"12656fac-3885-4a90-ac23-71378f32cc58","arxiv_id":"2509.00072","paper_version":4,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Post-cutoff decay is not a robust contamination signal because LLM-rephrased questions from the same source documents produce different temporal patterns than original cloze questions.","lead":"The paper shows that post-cutoff performance decay in LLMs, often used as a signal of benchmark contamination, changes dramatically depending on whether questions are kept as simple fill-in-the-blank or rewritten by another LLM. A smart generalist should read it because current methods for checking if models memorized test data may be unreliable and need rethinking.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"LLM transformation may alter tested knowledge/reasoning demands, confounding attribution of temporal pattern changes to question format alone.","rationale":"Reader's weakest assumption directly identifies the same point. The proposed check isolates whether observed temporal differences survive under stricter equivalence filtering, providing a direct test without requiring new experiments beyond re-analysis of existing data.","tokens_in":1646,"tokens_out":293,"duration_ms":20343,"concrete_test":"Sample 30 LiveCodeBench problems; obtain independent human ratings (3 annotators) of semantic equivalence and difficulty (1-5 Likert) between original cloze and LLM-transformed versions; recompute post-cutoff decay curves using only the subset with mean equivalence ≥4.0 and no significant difficulty shift; if the removal of temporal signal disappears or attenuates, the invariance assumption does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that LLM-driven transformations keep the underlying source material invariant in content, difficulty, and required capabilities while only varying surface construction (cloze vs. transformed). If transformations introduce rephrasing, added context, or shifts in problem structure that change what skills are tested, later models could show improved performance due to general advances in reasoning or data rather than absence of contamination. The abstract asserts invariance but the load-bearing risk is lack of explicit controls or metrics confirming equivalence on the LiveCodeBench problems used for the temporal comparison.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that post-cutoff performance decay in LLMs on benchmarks is not a reliable signal of contamination because it is highly sensitive to question construction. Using LiveCodeBench, the authors show that original cloze questions retrieved from source documents exhibit clear post-cutoff decay, while LLM-transformed versions of the same problems produce markedly different temporal patterns that remove the decay signal. They validate the effect on prior benchmarks reporting decay and provide mechanistic support via influence function analysis, concluding that more robust contamination probes are needed.","tokens_in":1758,"tokens_out":456,"duration_ms":21659,"significance":"If the central claim holds, the result would substantially weaken reliance on temporal decay as a contamination indicator and push the field toward format-robust evaluation methods. A strength is the combination of empirical validation on established benchmarks with influence-function analysis for mechanistic insight rather than purely correlational evidence.","major_comments":[{"comment":"Transformation experiment (Section 3 / Figure 2): The claim that LLM-driven transformations preserve the underlying source material invariant in content, difficulty, and required capabilities (while only varying surface format) is load-bearing for attributing temporal-pattern changes to question construction rather than shifts in tested skills. No explicit controls—such as semantic similarity metrics, human equivalence ratings, or difficulty calibration—are reported for the LiveCodeBench problems, leaving open the possibility that rephrasing or added context explains later-model gains via general capability advances.","section":"Section 3"}],"minor_comments":[{"comment":"The influence-function analysis would be clearer if the paper explicitly states the approximation method (e.g., LiSSA or conjugate gradient) and the number of samples used for the Hessian-vector products.","section":"Section 4"},{"comment":"Figure captions should include the exact number of problems per temporal bin and the precise definition of 'post-cutoff' date used for each benchmark.","section":"Figures 1-3"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a good fit for an AI evaluation venue; authors should be asked to confirm that the transformation LLM was not trained on any of the benchmark sources."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. We address the single major comment on the transformation experiment below and agree that additional controls will strengthen the manuscript.","responses":[{"response":"We agree that explicit controls would make the invariance claim more robust. The transformations were generated with a prompt that instructs the model to convert the original cloze-style retrieval into a standard problem statement while preserving the core programming task, test cases, and required reasoning steps. To directly address the concern, the revised manuscript will report (i) average cosine similarity of sentence embeddings between each original and transformed question (expected >0.85), (ii) a human equivalence study on a random subset of 50 problems in which independent raters score content fidelity and difficulty on 5-point scales, and (iii) a brief comparison of solution lengths and required algorithmic primitives. These additions will help separate format effects from capability shifts. The influence-function results already indicate that the format change alters token-level influence patterns in a manner consistent with reduced memorization rather than a broad increase in capability.","revision_made":"yes","referee_comment":"[Section 3] Transformation experiment (Section 3 / Figure 2): The claim that LLM-driven transformations preserve the underlying source material invariant in content, difficulty, and required capabilities (while only varying surface format) is load-bearing for attributing temporal-pattern changes to question construction rather than shifts in tested skills. No explicit controls—such as semantic similarity metrics, human equivalence ratings, or difficulty calibration—are reported for the LiveCodeBench problems, leaving open the possibility that rephrasing or added context explains later-model gains via general capability advances."}],"tokens_in":1258,"tokens_out":355,"duration_ms":28393,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that a simple LLM rewrite of LiveCodeBench problems eliminates the post-cutoff drop that has been treated as a contamination signal. This holds while pulling from the identical underlying documents, so the result directly questions how much we can read into temporal decay by itself.","headline":"Rewriting LiveCodeBench questions with an LLM removes the post-cutoff performance decay even on the same source documents, which weakens reliance on temporal patterns alone for spotting contamination.","tokens_in":2265,"tokens_out":135,"would_cite":true,"duration_ms":24928,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/RealityFromDistinction.lean","rs_theorem":"reality_from_one_distinction","paper_passage":"post-cutoff performance decay ... is highly sensitive to how benchmark questions are constructed, even if the underlying source material remains invariant"},{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"influence function analysis ... models were able to identify source documents among the most influential training documents"}],"headline":"Temporal contamination probe via question construction; no overlap with RS forcing chain or J-cost","alignment":"orthogonal","rationale":"Paper examines sensitivity of post-cutoff LLM performance decay to cloze vs. LLM-transformed question formats on arXiv/LiveCodeBench data, using influence functions for mechanistic insight. Domain is AI evaluation methodology. RS framework derives spacetime, c/ℏ/G, φ and 8-tick periodicity from bare distinguishability (reality_from_one_distinction, AbsoluteFloorClosure, AlexanderDuality, Cost.FunctionalEquation). No shared machinery, no contradiction possible.","tokens_in":53497,"confidence":"high","tokens_out":288,"duration_ms":10120,"cache_read_input_tokens":38528,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"The temporal decay signal for LLM benchmark contamination depends on question construction rather than source material alone.","keywords":["benchmark contamination","temporal signal","LLM evaluation","post-cutoff decay","question transformation","influence functions","LiveCodeBench","contamination detection"],"falsifier":"If LLM-transformed versions of problems from LiveCodeBench or similar benchmarks retain the same post-cutoff decay as the original cloze questions, the claim that the signal is sensitive to construction would not hold.","tokens_in":2555,"feed_emoji":"📉","tokens_out":615,"duration_ms":38279,"temperature":0.7,"pith_summary":"This paper challenges the view that post-cutoff performance drops in LLMs reliably indicate benchmark contamination from pre-training data. It demonstrates that the same underlying documents can yield starkly different temporal patterns depending on whether questions are direct fill-in-the-blank versions or LLM-transformed variants. On LiveCodeBench, the decay appears in cloze questions but vanishes after transformation, with influence function analysis offering a mechanistic account of the difference. A sympathetic reader would care because unreliable signals risk misjudging whether models generalize or merely recall specific phrasings.","feed_headline":"Question construction erases LLM contamination decay signal","feed_subtitle":"The same documents produce post-cutoff performance drops in cloze form but not after LLM transformation, showing the signal is format-driven","key_machinery":"LLM-driven transformation of questions from fixed source documents, which alters temporal performance patterns while preserving the underlying material.","core_discovery":"Post-cutoff performance decay has been interpreted as evidence of benchmark contamination via memorization of public data released before an LLM's training cutoff. The paper shows this decay is not invariant: cloze questions retrieved directly from source documents exhibit clear temporal decay on benchmarks such as LiveCodeBench, yet LLM-driven transformations of the identical problems remove the pattern. Influence function analysis supplies a mechanistic explanation for how question construction alters the observed temporal behavior.","pith_inferences":["The result raises the possibility that format-dependent artifacts affect other proposed signals of memorization beyond temporal decay.","Benchmark designers could routinely apply transformations to generate evaluation sets less vulnerable to format-specific contamination readings.","The finding invites direct tests on additional benchmarks to check whether the removal of decay generalizes across domains."],"forward_implications":["Temporal decay may fail to detect contamination reliably when benchmarks use transformed rather than cloze questions.","Simple LLM transformations can eliminate apparent contamination signals in existing benchmarks without changing source content.","Evaluation protocols require more robust contamination probes that do not hinge on a single question format.","Influence function analysis can identify how specific construction choices drive differences in temporal model behavior."],"fun_headline_variants":["Question construction alters LLM contamination signals","Cloze format reveals decay hidden in transformed questions","Same material different question types change decay patterns","Question transformation removes temporal contamination signal"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The LLM-driven transformation preserves the underlying source material without introducing or removing factors that independently alter the temporal performance pattern.","fun_headline_variants_meta":{"raw":{"variants":["Question construction alters LLM contamination signals","Cloze format reveals decay hidden in transformed questions","Same material different question types change decay patterns","Question transformation removes temporal contamination signal"]},"model":"grok-4.3","cost_usd":0.00994,"raw_usage":{"total_tokens":4392,"prompt_tokens":617,"num_sources_used":0,"completion_tokens":50,"cost_in_usd_ticks":99399500,"prompt_tokens_details":{"text_tokens":617,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3725,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":617,"tokens_out":50,"duration_ms":36580,"temperature":1.0,"reasoning_tokens":3725,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-18T21:07:09.318328+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"If LLM-transformed versions of problems from LiveCodeBench or similar benchmarks retain the same post-cutoff decay as the original cloze questions, the claim that the signal is sensitive to construction would not hold.","supporting_citations":[],"review_version":1}