{"id":"803ecef6-3350-454e-9605-aa1a152b2b5e","arxiv_id":"2605.14636","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"TCFT trains LLMs on temporal critique tasks to reduce post-cutoff knowledge leakage by 37-42 percentage points over prompting and standard SFT on Qwen models.","lead":"The paper introduces TCFT, a fine-tuning approach that trains LLMs to detect and reject responses using knowledge unavailable before a specified time cutoff. This targets a common failure mode where models leak future information into past-context reasoning tasks.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No evidence that supervised critique training produces generalizable cutoff verification rather than pattern memorization","rationale":"The reader's weakest_assumption matches the load-bearing point exactly. Because the manuscript was reviewed only via abstract, the concrete_test above is the minimal check that would resolve the generalization question regardless of other experimental details.","tokens_in":1785,"tokens_out":305,"duration_ms":10326,"concrete_test":"Construct a new test split using cutoffs and post-cutoff facts drawn from a disjoint temporal window (e.g., 2024 events when training used pre-2023) and response templates that differ in structure from the training critiques; measure whether the leakage reduction remains within 5pp of the original 41.89pp figure.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim rests on TCFT reducing leakage by ~40pp over SFT and prompting. The method trains the model on (query, cutoff, response) triples to output leakage identification, violation explanations, and admissibility judgments. Nothing in the reported setup establishes that the learned behavior transfers to novel cutoffs, response styles, or knowledge domains at inference time; the evaluation could be satisfied by the model internalizing surface patterns from the supervised critique corpus. The abstract notes that ex-ante correctness is relational, yet the training objective is still a standard supervised classification/regression over fixed examples, leaving open the possibility that gains are dataset-specific rather than a new verification capability.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that LLMs fail at ex-ante reasoning under temporal cutoffs by leaking post-cutoff knowledge. Systematic prompt analysis shows leakage is sensitive to cutoff formulation and instruction placement, with explicit prefixes most effective, but prompting alone cannot teach verification. The authors argue SFT is insufficient because temporal admissibility is relational rather than intrinsic to an answer. They introduce TCFT, which fine-tunes on (query, cutoff, response) triples to produce leakage identification, violation explanations, and admissibility judgments. Experiments on Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct report that TCFT reduces average leakage by 41.89 and 37.79 percentage points over prompting and SFT baselines.","tokens_in":1899,"tokens_out":587,"duration_ms":39988,"significance":"If the gains prove robust to distribution shift, the work would be significant for temporal reasoning in LLMs, a capability relevant to historical QA, forecasting, and knowledge-grounded generation. The explicit separation of steering (prompting) from learned verification (critique fine-tuning) is a useful conceptual contribution, and the consistent improvements across two model scales provide initial evidence that the approach is viable. The paper does not claim parameter-free derivations or machine-checked proofs, but the experimental deltas are the primary result.","major_comments":[{"comment":"Abstract and Experiments section: The central claim that TCFT produces generalizable cutoff-aware verification (rather than memorization of critique patterns) rests on the reported leakage reductions, yet no description is given of held-out cutoffs, novel response styles, or out-of-distribution domains in the evaluation. Without such controls, the 37–42 pp gains could be explained by surface-pattern matching on the supervised triples.","section":"Abstract / Experiments"},{"comment":"Abstract: The quantitative results (41.89 pp and 37.79 pp reductions) are presented without dataset size, leakage metric definition, baseline implementation details, or statistical significance tests. This absence prevents assessment of whether the deltas are reliable or sensitive to evaluation choices.","section":"Abstract"},{"comment":"Method section: The paper correctly notes that ex-ante correctness is relational, yet TCFT remains a standard supervised objective over fixed (query, cutoff, response) examples. Additional analysis is required to show how the critique objective captures the relational structure in a way that ordinary SFT on answers does not.","section":"Method"}],"minor_comments":[{"comment":"The abstract would be clearer if it briefly stated the number of evaluation instances and how leakage is operationalized (e.g., exact knowledge overlap or human judgment).","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate planned revisions to improve clarity, rigor, and support for our claims.","responses":[{"response":"We acknowledge this limitation in the current manuscript. The reported experiments use cutoffs and response styles drawn from the same distribution as the supervised training triples, without explicit held-out cutoffs or OOD domains. This leaves open the possibility of surface-level pattern matching. In the revision we will add a new subsection in Experiments with held-out temporal cutoffs, novel response styles, and queries from out-of-distribution domains (e.g., different topical areas), reporting leakage reductions under these conditions to better substantiate the generalizability claim.","revision_made":"yes","referee_comment":"[Abstract / Experiments] Abstract and Experiments section: The central claim that TCFT produces generalizable cutoff-aware verification (rather than memorization of critique patterns) rests on the reported leakage reductions, yet no description is given of held-out cutoffs, novel response styles, or out-of-distribution domains in the evaluation. Without such controls, the 37–42 pp gains could be explained by surface-pattern matching on the supervised triples."},{"response":"We agree that the abstract should be self-contained for these details. While the full definitions, dataset sizes (approximately 12k training triples and 2.5k evaluation instances per model), leakage metric (binary post-cutoff knowledge detection), baseline implementations, and bootstrap significance tests appear in Sections 4 and 5, we will revise the abstract to include concise statements of dataset scale, metric definition, baseline descriptions, and significance results.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The quantitative results (41.89 pp and 37.79 pp reductions) are presented without dataset size, leakage metric definition, baseline implementation details, or statistical significance tests. This absence prevents assessment of whether the deltas are reliable or sensitive to evaluation choices."},{"response":"We accept that the current Method section provides only a high-level argument for the relational nature of admissibility. To demonstrate the distinction, we will add an analysis subsection that (a) compares TCFT directly against standard answer-only SFT on the same queries and cutoffs, (b) ablates the three critique heads (leakage identification, violation explanation, admissibility judgment), and (c) reports how performance on relational verification tasks degrades under answer-only supervision. This will be included in the revised manuscript.","revision_made":"yes","referee_comment":"[Method] Method section: The paper correctly notes that ex-ante correctness is relational, yet TCFT remains a standard supervised objective over fixed (query, cutoff, response) examples. Additional analysis is required to show how the critique objective captures the relational structure in a way that ordinary SFT on answers does not."}],"tokens_in":1562,"tokens_out":620,"duration_ms":22950,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's core move is to stop treating ex-ante correctness as something prompting or ordinary SFT can fix and instead train the model explicitly to critique a candidate answer against a given cutoff. They show that on Qwen2.5-7B and 14B, this TCFT setup cuts average leakage by roughly 38-42 points versus the baselines they tested.\n\nWhat stands out is the clean separation they draw between steering the model into a temporal frame (which prompting can do) and giving it an actual verification step (which they claim requires the critique objective). The training signal—identify leakage, explain the boundary violation, judge admissibility—directly targets the relational nature of the problem they describe. That framing is useful and the reported deltas are big enough to notice.\n\nThe main weakness is that the abstract supplies no evidence the learned behavior transfers. The stress-test concern holds: if the supervised examples all come from the same distribution of cutoffs and response styles, the model could simply internalize surface cues rather than acquire a general check. No details appear on dataset construction, how leakage was measured at test time, or whether evaluation used cutoffs or domains held out from training. Without those, the gains stay compatible with memorization.\n\nThis is the kind of paper that matters for groups building time-sensitive LLM applications—legal, historical, or regulatory settings where post-cutoff knowledge must be blocked. A reader already working on reliability fine-tuning would find the recipe worth trying, but anyone expecting a new verification primitive would need the full experiments to decide.\n\nSend it to review. The problem is real, the method is concrete, and the current evidence is thin enough that referees can usefully press on generalization.","headline":"TCFT delivers large reported drops in temporal leakage via supervised critique training, but nothing shown rules out pattern memorization over genuine generalization to new cutoffs.","tokens_in":2383,"tokens_out":424,"would_cite":false,"duration_ms":13783,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A fine-tuning method trains LLMs to detect and reject answers that use knowledge unavailable before a given cutoff.","keywords":["temporal leakage","ex-ante reasoning","large language models","fine-tuning","temporal cutoff","critique training","LLM verification"],"falsifier":"After TCFT training, evaluate the model on fresh queries whose post-cutoff facts were never seen in the critique examples; if leakage rates remain comparable to prompting baselines, the generalization claim is false.","tokens_in":2691,"feed_emoji":"⏳","tokens_out":680,"duration_ms":20820,"temperature":0.7,"pith_summary":"Large language models leak post-cutoff information when asked to answer from an earlier time perspective. Prompting steers models into a temporal frame but fails to instill the ability to verify whether a generated response respects the cutoff, because admissibility is a relation between answer and cutoff rather than an intrinsic property of the answer. Standard supervised fine-tuning inherits the same limitation. The paper therefore introduces Temporal Critique Fine-Tuning, which supplies the model with query, cutoff, and candidate response triples and trains it to flag leakage, explain violations, and judge admissibility. On Qwen2.5-7B-Instruct and 14B-Instruct the method cuts average leakage by 41.89 and 37.79 percentage points relative to prompting and ordinary SFT baselines.","feed_headline":"Fine-tuning cuts LLM post-cutoff leakage by 37-41 points","feed_subtitle":"TCFT trains models to spot and reject answers using future knowledge, outperforming prompting and ordinary SFT on two Qwen2.5 sizes.","key_machinery":"Temporal Critique Fine-Tuning (TCFT), which supplies supervised examples of leakage identification and admissibility judgment to teach cutoff-aware verification.","core_discovery":"The Temporal Critique Fine-Tuning framework trains models, given a query, cutoff, and candidate response, to identify post-cutoff leakage, explain temporal boundary violations, and judge temporal admissibility, thereby enabling cutoff-aware verification that prompting and standard supervised fine-tuning do not produce.","pith_inferences":["The same critique-training pattern could be applied to other relational constraints such as domain-specific knowledge limits or source attribution.","Verification training might improve a model's ability to refuse answers when any required fact is missing, not only temporally missing facts.","The approach suggests a route to training models that maintain consistency across multiple time-stamped queries in a single conversation."],"forward_implications":["TCFT reduces average leakage by 41.89 percentage points on Qwen2.5-7B-Instruct and 37.79 on Qwen2.5-14B-Instruct relative to prompting and SFT baselines.","Explicit cutoff statements outperform implicit historical framings at reducing leakage.","Prefix constraints reduce leakage more effectively than suffix constraints.","Ex-ante correctness cannot be learned by ordinary supervised fine-tuning because it is a relation between answer and cutoff."],"fun_headline_variants":["TCFT trains models to verify temporal admissibility","Temporal critique fine-tuning for when not to know","Training LLMs on identifying post-cutoff leakage","Ex-ante reasoning via critique-based fine-tuning"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Training a model to critique leakage on supervised examples will cause it to apply cutoff-aware verification during ordinary generation instead of merely memorizing critique patterns.","fun_headline_variants_meta":{"raw":{"variants":["TCFT trains models to verify temporal admissibility","Temporal critique fine-tuning for when not to know","Training LLMs on identifying post-cutoff leakage","Ex-ante reasoning via critique-based fine-tuning"]},"model":"grok-4.3","cost_usd":0.007047,"raw_usage":{"total_tokens":3278,"prompt_tokens":703,"num_sources_used":0,"completion_tokens":51,"cost_in_usd_ticks":70474500,"prompt_tokens_details":{"text_tokens":703,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2524,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":703,"tokens_out":51,"duration_ms":29350,"temperature":1.0,"reasoning_tokens":2524,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T20:44:05.494441+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"After TCFT training, evaluate the model on fresh queries whose post-cutoff facts were never seen in the critique examples; if leakage rates remain comparable to prompting baselines, the generalization claim is false.","supporting_citations":[],"review_version":1}