{"id":"f537be40-ed8a-4b01-b2b6-13d0d1f92554","arxiv_id":"2608.09106","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"LexKairos provides a nine-sub-task Chinese legal temporal benchmark and shows that current LLMs, despite strong case chronology performance, remain weak at statutory version recall and procedural time-limit reasoning.","lead":"This paper introduces LexKairos, a benchmark that tests how well large language models handle time in Chinese legal reasoning, covering statutory dates, case timelines, and procedural deadlines. It evaluates eight models and finds that even the best one struggles with precise statutory metadata recall and complex time-limit reasoning.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The benchmark's headline scores depend on gold labels for Tasks 2 and 3 that rest on an undocumented LLM-assisted audit; without inter-annotator reliability and prompt-derivability checks, the measurement validity of LexKairos is unestablished.","rationale":"The reader's conditional acceptance already identified label reliability as the weak assumption. I agree and sharpen it: the missing quantity is not just 'are labels correct' but 'are labels derivable from the exact model input.' For a benchmark whose purpose is to evaluate reasoning from case facts, a gold label that depends on information absent from the prompt measures something else. The proposed check is decisive because it directly quantifies label noise and its effect on the reported metrics. I do not see a more load-bearing problem: the error analysis is careful, the truncation analysis is honest, and the task design is well-motivated. The absence of inter-annotator agreement is a gap in evidence, not proof of fraud. The verdict should remain conditional: the paper is publishable if the authors supply the reliability/derivability audit and a pinned release.","tokens_in":17239,"tokens_out":6915,"duration_ms":73150,"concrete_test":"Randomly sample 50 instances per Task 2 sub-task and all 289 Task 3 instances; have two independent legal experts, blinded to the existing gold, re-derive the gold answer from the same source judgment and then, separately, from the exact prompt text. Compute Cohen's kappa between experts and against the published gold, and recompute Table 2 scores restricted to instances where both experts and the gold agree. If kappa is below 0.8, or if expert-from-prompt accuracy is substantially below 100%, or if the best-model ranking or the 'notable limitations' conclusion changes on the agreed subset, the benchmark's validity claim is not supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that LexKairos measures legal temporal capabilities and that current LLMs are notably limited—is an empirical measurement claim, so the gold labels are the linchpin. Section 3.3 and Appendix A state that Task 2's 293-instance structured database was built with DeepSeek-V3.2 event extraction followed by 'meticulous manual audit,' and that Task 3's 180 and 109 instances were refined via DeepSeek-V3.2 extraction and then expert review. The paper reports no inter-annotator agreement, no number or qualifications of auditors/experts, no audit protocol, and no evidence that each gold answer is derivable from the prompt alone. In fact, the Task 3 prompts in Appendix B (Tables 11–14) show abridged case facts; if the gold labels were set with access to the full source judgment, then the model is being asked to answer from a strict subset of the information the gold annotator used, making low scores uninformative rather than evidence of limitation. Because DeepSeek-V3.2 was used to construct the gold and DeepSeek-R1/V4 are among the evaluated models, any systematic extraction bias could also favor or disfavor an entire model family. The paper's Limitations section asserts that expert verification was applied to every instance, but that assertion is not accompanied by measurable reliability evidence.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript introduces LexKairos, a benchmark for evaluating the temporal capabilities of LLMs in the Chinese legal domain, organized into three dimensions (statutory temporal knowledge, case temporal modeling, and statute-case temporal reasoning) with nine sub-tasks. The data are constructed from statutes and real civil judgments, and the authors evaluate eight LLMs under vanilla, CoT, and thinking settings, reporting that Gemini-3-Flash (Thinking) achieves the highest overall score (84.57) while all models show marked weaknesses in statutory metadata recall and procedural time-limit reasoning. The paper also presents a fine-grained error analysis for Effective Version Identification and a token-efficiency study of task-specific legal prompting on the procedural tasks.","tokens_in":17467,"tokens_out":5761,"duration_ms":59456,"significance":"If the benchmark's measurements are valid, LexKairos addresses a genuine gap: existing legal AI benchmarks focus on semantic and logical reasoning while largely ignoring the temporal dimension, which is objectively verifiable and practically important. The task taxonomy is clear, the evaluation protocol is transparent (temperature 0, 8,000-token output limit, explicit prompts in Appendix B), Task 1 is generated by a rule-based pipeline with manual filtering, and the public release of data and code supports reproducibility. The fine-grained error decomposition for Task 1-2 is a useful contribution, and the central empirical finding—that strong general LLMs remain unreliable on precise statutory metadata and procedural deadlines—is plausible and interesting. However, the benchmark's validity hinges on the gold-label construction for Tasks 2 and 3; the paper currently does not supply enough evidence that those labels are correct, complete, and derivable from the prompts alone.","major_comments":[{"comment":"The gold labels for Task 2 (the 293-instance structured database) and Task 3 (180 and 109 instances) are produced by DeepSeek-V3.2 extraction followed by manual audit or expert review, but the paper reports no inter-annotator agreement, no number or qualifications of the auditors/experts, and no audit protocol. Because every Task 2 and Task 3 score in Table 2 is a measurement against these labels, this is load-bearing. The Limitations section asserts expert manual verification for every test instance, but without measurable reliability evidence this remains an assertion. The authors should report Cohen's kappa or equivalent on a re-annotated sample, describe the audit procedure, and address the risk that systematic extraction bias from DeepSeek-V3.2 could shift cross-model comparisons, since DeepSeek-R1 and DeepSeek-V4 are among the evaluated models.","section":"Section 3.3 / Appendix A / Table 2"},{"comment":"The task prompts present abridged case facts (indicated by ellipses), while Appendix A states that Task 3 gold answers were audited and refined by legal experts using the full source judgments. If gold annotations used information absent from the prompt, low model scores reflect incomplete input rather than deficient temporal reasoning; conversely, if the gold is derivable from the prompt alone, the paper should demonstrate this. As written, I could not verify that every gold answer is derivable from the prompt alone. The authors should either include the full relevant fact text in the released benchmark or provide a derivability check, such as an annotation sample answered solely from the prompt, because this is necessary for the validity of Tasks 2 and 3.","section":"Appendix B, Tables 11-14 / Section 3.3"},{"comment":"No confidence intervals or statistical tests are reported, and Task 3 has only 180 and 109 instances. Headline claims such as \"Gemini-3-Flash achieves the strongest overall performance\" and the cross-model comparisons for Task 3 rest on point estimates whose sampling variability is unknown; with n=109, a difference of a few F1 points can easily be within chance. The paper should add bootstrap confidence intervals or exact tests, at least for Task 3 and for the overall ranking, so that the reader can distinguish reliable differences from noise.","section":"Section 4.3 / Table 2"}],"minor_comments":[{"comment":"The \"Overall\" column is not defined; the paper should state whether it is an unweighted mean of the nine sub-task scores and whether sub-tasks with different instance counts are weighted.","section":"Table 2"},{"comment":"The text says \"As shown in Figure 4\" when discussing the three-way error decomposition, but the figure captions indicate that Figure 3 is the error-decomposition figure and Figure 4 is the sub-category distribution; this cross-reference should be corrected.","section":"Section 4.4 / Figure 3 and Figure 4"},{"comment":"The text states that Task 2-1 uses three Allen interval relations (after, meet, during), but the option set in Table 6 includes a fourth relation (Event B strictly occurs before Event A); the description should be updated to match the actual option set.","section":"Section 3.2.2 / Table 6"},{"comment":"The relation between the 293 verified instances in Appendix A and the much larger Task 2 volumes (450, 550, 500, 594) should be clarified, presumably multiple SQL-generated questions per case; this is currently left implicit.","section":"Table 1 / Appendix A"},{"comment":"Task 2-4 is listed as SLC & MLC with a single data volume of 594; the paper should provide the split between the single-label and multi-label formats.","section":"Table 1"}],"recommendation":"major_revision","confidential_remarks":"The paper is within scope, and the resource is potentially valuable. My main reservation is that the benchmark's central empirical claims depend on gold-label validity, which is not yet documented; the requested additions (inter-annotator reliability, audit protocol, prompt-derivability check, and confidence intervals) are standard validity requirements for a benchmark paper rather than optional enhancements."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"LexKairos is a real contribution: it is the first benchmark I know that combines statutory lifecycle tracking, case chronology modeling, and procedural time-limit reasoning in one suite, and it evaluates eight LLMs across three inference settings in a transparent way (temperature 0, 8000 tokens, clear metrics). The error analysis on version identification is genuinely useful—the 'tag only' failure mode and the cross-model asymmetry between GPT-5.4 and LegalOne-8B are interesting findings worth building on.\n\nThe soft spots are concentrated in the gold-label construction. Task 2's 293-instance database and Task 3's 109–180 instances rely on DeepSeek-V3.2 extraction followed by manual audit or expert review. The paper reports no inter-annotator agreement, no number or qualifications of the auditors/experts, and no detailed audit protocol. That would be a minor concern for a large benchmark, but here the label correctness is load-bearing: these are the measurement instruments for the empirical claim that current LLMs struggle with legal temporal reasoning.\n\nMore worrying, the stress-test note is right about the prompt/label mismatch. The Task 3 prompts in Appendix B show heavily abridged case facts (ellipses), while the gold labels were almost certainly set with access to the full source judgment. If that is the case, then a model answering from the abridged prompt is working with less information than the annotator had. Low scores on those tasks would reflect the information asymmetry as much as any real limitation. The paper should demonstrate that each gold answer is derivable from the prompt as given, or provide the full case text in the prompts. This is a fixable problem, but it is a real one.\n\nTask 3's small sizes and the absence of confidence intervals or significance tests also limit how much we can read into the ranking differences between models, though the overall pattern (thinking mode helps, but version recall stays weak) is probably robust.\n\nOn balance, this paper deserves a serious referee. It is honest about its limitations, the code and data are promised, and the task taxonomy is a useful contribution to legal AI evaluation. My recommendation: send it to review, but ask the authors for a much more detailed label-construction appendix, inter-annotator reliability numbers, and a prompt-derivability analysis for Tasks 2 and 3. If they can show the gold labels are verifiable from the prompts, this becomes a strong benchmark paper.","headline":"LexKairos fills a real gap in legal AI evaluation, but the measurement validity of Tasks 2 and 3 hinges on an under-documented gold-label pipeline and a prompt/label information mismatch that needs to be addressed before the scores can be trusted.","tokens_in":18025,"tokens_out":2930,"would_cite":true,"duration_ms":26630,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"LexKairos, a new Chinese legal benchmark, shows that even the strongest LLMs struggle to recall exact statutory dates and to reason over procedural time limits.","keywords":["legal temporal reasoning","benchmark","Chinese law","LLM evaluation","statutory temporal knowledge","case chronology","procedural time limits","temporal reasoning"],"falsifier":"An independent re-annotation study: take a random sample of the 293-instance temporal database and the 289 procedural time-limit instances, have legal experts annotate them from scratch without seeing the original labels, and measure agreement. Low inter-annotator agreement, or systematic disagreement with the published labels, would show the benchmark does not measure what it claims.","tokens_in":17010,"feed_emoji":"⚖️","tokens_out":5337,"duration_ms":45548,"temperature":0.7,"pith_summary":"This paper introduces LexKairos, a benchmark for measuring how well large language models handle time in Chinese legal settings. It organizes legal time into three dimensions—statutory temporal knowledge, case temporal modeling, and statute-case temporal reasoning—and builds nine subtasks from real judicial cases and statutes. The paper evaluates eight LLMs under vanilla, chain-of-thought, and thinking settings, and reports that even the strongest model, Gemini-3-Flash with thinking mode, reaches only 84.57 overall, with clear weaknesses on recalling exact effective dates and reasoning over procedural deadlines. The intended contribution is a reliable, multi-dimensional test for an underexplored capability, plus evidence that current models are not dependable for legal tasks where 'when' matters.","feed_headline":"Top LLMs fail legal time tests, new benchmark finds","feed_subtitle":"LexKairos tests nine subtasks of Chinese law time; even top models miss exact dates and deadlines.","key_machinery":"The central object is the LexKairos benchmark itself, with its capability taxonomy: three primary dimensions (Statutory Temporal Knowledge, Case Temporal Modeling, Statute-Case Temporal Reasoning) mapped to nine subtasks. The conceptual machinery is the 'Legal Time' distinction between normative time (statute versions, effective periods, deadlines) and factual time (event anchoring, timeline construction, period calculation), together with Allen's interval relations used to define event-relation labels. The benchmark's curation pipeline—rule-based synthesis from statutory metadata, SQL-based QA generation over a manually audited event database, and expert-reviewed procedural time-limit cases—converts the taxonomy into measurable scores.","core_discovery":"The central claim is that legal temporal capability is a distinct, measurable competence that existing legal AI benchmarks overlook, and that LexKairos operationalizes it. The paper defines 'Legal Time' at two levels—statutes (effective dates, versions, validity) and cases (event timestamps, timelines, durations)—and builds nine subtasks to test recall, modeling, and joint reasoning. Evaluating eight models, it finds that case chronology tasks are comparatively easier, statutory metadata recall and procedural time-limit reasoning remain hard, and thinking mode helps but does not fix a persistent precision deficit in version-identifier recall. The paper's discovery, in its own terms, is a capability taxonomy and a measurement showing current LLMs are not temporally reliable in law.","pith_inferences":["A natural extension would be to test whether the reported hierarchy—case chronology easiest, statutory metadata hardest—holds in other legal systems, such as US or EU statutes with different revision conventions.","The 'tag only' error asymmetry between omission-prone and hallucination-prone models suggests calibration strategies tailored to each model family could be more effective than a single prompting recipe.","One could use LexKairos's structured task taxonomy as a training curriculum: models that first master event anchoring and timeline construction might then improve on statute-case reasoning.","If the gold labels inherit biases from the automated extraction model, the benchmark's scores could partly reflect that model's temporal blind spots; independent re-annotation would clarify."],"forward_implications":["If LexKairos is a valid measure, legal temporal capability should be treated as a separate axis in legal LLM evaluation, distinct from semantic and logical reasoning.","The reported scores imply that legal assistants built on current LLMs should not be trusted for version identification or peremptory-period decisions without external verification.","Thinking mode consistently improves scores but does not remove the version-identifier precision deficit, so the bottleneck is knowledge recall, not reasoning effort.","Task-specific legal prompting achieves comparable performance on procedural time-limit reasoning with up to 3.4x shorter outputs, suggesting structured prompts are a token-efficient alternative to unconstrained thinking mode.","The benchmark's day-level granularity restriction means multi-scale temporal reasoning (mixed month/day, sub-day) remains untested."],"supporting_citations":[{"why":"Defines the interval relations (after, meet, during) used to construct and label the event-relation subtask 2-1.","marker":"(Allen, 1983)"},{"why":"Source of the real-world Chinese judicial cases from which Task 2 and Task 3 instances are derived.","marker":"(China Judgments Online, 2013)"},{"why":"LawBench, the comprehensive Chinese legal benchmark whose omission of temporal dimensions motivates LexKairos's design.","marker":"(Fei et al., 2024)"},{"why":"LexTime, the prior legal temporal benchmark limited to event ordering, which LexKairos extends to statutory and procedural time.","marker":"(Barale et al., 2025)"},{"why":"General temporal reasoning benchmark whose task formats inform the event-ordering and time-calculation subtasks.","marker":"(Tan et al., 2023)"},{"why":"Provides temporal reasoning evaluation methodology, including chronological ordering and temporal NLI, adapted for legal content.","marker":"(Wang and Zhao, 2024)"}],"fun_headline_variants":["LLMs flunk legal time: new benchmark LexKairos","LexKairos: LLMs still can't handle legal deadlines","Legal AI lacks temporal sense, new benchmark shows","Top LLMs trip on legal time, LexKairos reveals","Nine legal time tasks expose LLM weaknesses"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The benchmark's measurements rest on the accuracy of the gold labels for case-chronology and procedural-time-limit tasks, which were produced by an automated extraction system and then reviewed by experts; if that review missed systematic temporal errors, every reported score on those tasks could be wrong.","fun_headline_variants_meta":{"raw":{"variants":["LLMs flunk legal time: new benchmark LexKairos","LexKairos: LLMs still can't handle legal deadlines","Legal AI lacks temporal sense, new benchmark shows","Top LLMs trip on legal time, LexKairos reveals","Nine legal time tasks expose LLM weaknesses"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000598,"raw_usage":{"total_tokens":2772,"prompt_tokens":894,"completion_tokens":1878,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":510,"completion_tokens_details":{"reasoning_tokens":1796}},"tokens_in":510,"tokens_out":1878,"duration_ms":14381,"temperature":1.0,"reasoning_tokens":1796,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T23:25:24.590391+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"An independent re-annotation study: take a random sample of the 293-instance temporal database and the 289 procedural time-limit instances, have legal experts annotate them from scratch without seeing the original labels, and measure agreement. Low inter-annotator agreement, or systematic disagreement with the published labels, would show the benchmark does not measure what it claims.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Source of the real-world Chinese judicial cases from which Task 2 and Task 3 instances are derived."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"LawBench, the comprehensive Chinese legal benchmark whose omission of temporal dimensions motivates LexKairos's design."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"General temporal reasoning benchmark whose task formats inform the event-ordering and time-calculation subtasks."}],"review_version":1}