{"id":"c591fc14-6a36-413d-a9c1-b157c0b2c40f","arxiv_id":"2603.01121","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"HVR-Met reports pass rates of 71.86% on index calculation, 79.52% on plotting, and 85% on final reports using a hypothesis-verification-replanning agent loop.","lead":"HVR-Met is a team of AI agents that diagnose extreme weather by forming a hypothesis, checking data and maps, and replanning when the evidence does not fit. It also introduces a benchmark of 100 weather events and 250 smaller tasks for index calculation and plotting.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The claimed 85% final-report pass rate is arithmetically impossible given Table 1's mean Report score of 3.94 and the stated pass threshold of >4.","rationale":"I reviewed the full paper and the reader's verdict. The reader's weakest_assumption is benchmark contamination (same-corpus overlap between the sub-task benchmark and the Guideline Library). That is a plausible concern, but I find a more fundamental and decisive internal inconsistency: the abstract and Contributions tout an 85% pass rate for final reporting, yet Table 1's Final Report mean is 3.94 on a 0–5 scale with a pass threshold of >4. Simple arithmetic shows that such a pass rate is impossible given that mean, regardless of benchmark integrity. This is not an external challenge; it is a direct contradiction within the manuscript's own numbers. It invalidates the central quantitative claim as stated. The paper does have strengths: a multi-agent design, a human-validated rubric, and an attempt at a benchmark. But the headline result cannot be verified without correction of the reported scores or the pass definition. My recommendation moves the verdict from CONDITIONAL to REJECT because the central claim is internally inconsistent and the burden is on the authors to supply consistent evidence. If the authors provide corrected numbers or clarify the pass criterion, the paper might be reconsidered, but as written it cannot be accepted.","tokens_in":16147,"tokens_out":5732,"duration_ms":55564,"concrete_test":"Request from the authors the per-case Report scores for the 100 end-to-end events (or the raw judge outputs used to compute Table 1) and recompute the pass rate with the stated threshold '>4'. If the pass rate is 85%, compute the implied mean and compare it to 3.94; if they conflict, request corrected numbers or an explicit reconciliation (e.g., if the threshold is actually '≥4' or if 'pass' is defined differently). Also confirm that the 85% figure refers to the same 100 cases and the same Report scoring rubric, not a different subset or a different metric.","verdict_should_be":"REJECT","load_bearing_attack":"The paper's headline claim (Contributions, §1) asserts a 85% pass rate for final reporting. Table 1 (§5.2) reports the full framework (GPT-5) with a Final Report mean score of 3.94 across 100 end-to-end cases, and §4 states 'a score greater than 4 is considered a pass.' These two numbers cannot both be true. If 85% of reports pass, at least 85 cases must score strictly above 4 (minimum pass score = 4+ε, ε>0), while the remaining 15 cases score at most 4. The minimum possible mean under these constraints is (85(4+ε)+15·4)/100 = 4 + 0.85ε, which is strictly greater than 4 for any positive ε. With integer rubric scores (pass = 5, fail ≤ 4), the minimum possible mean is 4.85. Even in the most permissive fractional-score interpretation, a mean of 3.94 is below 4 and therefore incompatible with an 85% pass rate at threshold >4. This internal inconsistency directly undermines the central quantitative claim of 'robust capacity to assist forecasters.' The error likely stems from a misstated threshold, a different definition of 'pass,' or a reporting discrepancy, but as written the two numbers are irreconcilable.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes HVR-Met, a multi-agent system for extreme weather diagnosis that combines a semi-automatically constructed Guideline Library (derived from 584 papers and expert-verified), seven specialized agents, and a Hypothesis–Verification–Replanning (HVR) loop. The authors also introduce a benchmark consisting of 100 end-to-end extreme weather cases, 150 index-computation tasks, and 100 figure-generation tasks, and report high performance for a GPT-5-based configuration, including pass rates of 71.86% for index computation, 79.52% for figure generation, and 85% for final reporting. The central contribution claims are the HVR mechanism, the domain-knowledge integration, the new benchmark, and the reported quantitative results.","tokens_in":16493,"tokens_out":6652,"duration_ms":63442,"significance":"If the reported results are reproducible and the benchmark is not contaminated, the paper would be a meaningful advance in agentic meteorological diagnosis: it integrates domain knowledge acquisition, iterative verification, and a fine-grained evaluation suite, with expert validation by five senior forecasters. The ablation study usefully demonstrates the contribution of the Decomposer, Image Checker, Diagnostician, and knowledge bases. However, the absence of code/data release, the unresolved benchmark-leakage question, and the lack of a clear derivation for the headline pass-rate figures prevent a full assessment of the claimed robustness.","major_comments":[{"comment":"The paper states in §4 that 'a score greater than 4 is considered a pass' and in Table 1 reports a mean Final Report score of 3.94 for the full GPT-5 framework, yet the Contributions (§1) and Conclusion claim an 85% final-report pass rate. No pass-rate computation, score distribution, or exact threshold definition is given. Under a natural integer 0–5 rubric, a mean of 3.94 is incompatible with an 85% pass rate when pass requires a score of 5 (minimum possible mean = 0.85×5 = 4.25). If fractional scores are allowed, the mean alone does not establish the 85% figure. Please report the actual pass-rate calculation, the score discretization, the distribution, and reconcile the headline number with Table 1.","section":"§1, §4, Table 1"},{"comment":"The Guideline Library is built from 584 papers (§3.1), and the sub-task benchmark is 'derived from 584 extreme weather analysis papers' (§4). The gold-standard figures, QA pairs, and plotting captions are thus sampled from the same corpus that supplies the system's knowledge base. This creates a likely leakage channel: for the Plotter sub-task, the system is directly given captions from that corpus, and the index QA pairs target indices catalogued in the library. The statement that the end-to-end dataset is independent from the sub-task corpus does not address this, because the sub-task benchmarks themselves are not shown to be independent of the knowledge-base corpus. Please provide a concrete decontamination analysis (e.g., a source-level or temporal split, or a list confirming disjoint papers) and, if leakage remains, re-evaluate the affected claims.","section":"§3.1 vs §4"},{"comment":"The aggregate sub-task accuracies of 71.86% and 79.52% are not traceable to the reported experimental results. Table 5 and Figure 4 give per-difficulty-tier accuracies only, and no formula is provided for combining them into the claimed aggregates. Additionally, the acceptance criterion in §4 is written as 'GT−Reply / GT < 0.05', which lacks an absolute-value operator (and the GT=0 case uses 'Reply < 0.05'). Please define the aggregate metric explicitly, report the number of tasks in each tier, and include per-index/per-figure-type breakdowns so the headline numbers are reproducible.","section":"§5.3, §4"},{"comment":"No code, benchmark items, model outputs, or evaluation scripts are released. The human-alignment validation on 20% of the end-to-end data is described only as 'high correlation', with no coefficient, agreement metric, or methodology. For a paper whose central claims are quantitative and operational, this level of disclosure prevents independent verification. Please provide a public artifact with the benchmark, scoring code, and detailed human-alignment statistics, or otherwise substantially expand the supplementary material.","section":"General reproducibility"}],"minor_comments":[{"comment":"Title misspells 'Replanning' as 'Replaning'; the correct spelling is used in the body.","section":"Title"},{"comment":"Heading 'Automic-level' should be 'Atomic-level'; also 'calcalation' is a typo for 'calculation'.","section":"§5.3"},{"comment":"The relative-error formula should use an absolute value: |GT−Reply|/|GT| < 0.05; for GT=0, the absolute error constraint should be |Reply| < 0.05.","section":"§4"},{"comment":"The references to Figure 2 and Figure 3 in the text appear mismatched with the actual figures; please check the numbering throughout.","section":"Figures"},{"comment":"A Limitations section discussing benchmark scope, potential language/regional bias, and the system's dependence on the knowledge-base corpus would improve clarity.","section":"General"}],"recommendation":"major_revision","confidential_remarks":"The paper addresses an interesting and timely application of LLM agents to operational meteorology, and the system architecture is sensible. My main concerns are the unresolved benchmark-leakage question and the unexplained discrepancy between the reported pass rate and the mean final-report score. These are fixable in principle if the authors can provide the evaluation artifacts and a clear decontamination analysis. I would not reject the paper outright, but the current evidence is insufficient to support the headline claims."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Thanks for the report. I read the preprint carefully, and I largely agree with your conditional verdict, with one addition: the arithmetic problem is worse than you say.\n\nThe paper does something genuinely useful: it builds a multi-agent system for extreme-weather diagnosis with a detailed HVR loop, a guideline library distilled from 584 papers and vetted by five forecasters, and a new benchmark covering 100 end-to-end events plus 150 index-calculation and 100 plotting subtasks. The system prompts and scoring rubrics are unusually complete, and the end-to-end dataset is independent of the sub-task corpus. That last point is important—it means the main evaluation is not automatically contaminated.\n\nBut the central quantitative claim does not hold up. The abstract and conclusion say HVR-Met achieves an 85% pass rate for final reporting. Section 4 says a score >4 is a pass. Table 1 gives GPT-5 a mean Final Report score of 3.94. Those three facts cannot coexist. With 100 cases, if 85 pass at >4, the minimum possible mean is >4; if scores are integers, the minimum is 4.85. A mean of 3.94 is mathematically incompatible. This isn't a quibble about rounding—it's the paper's headline result. Until the authors clarify the threshold, the scoring scale, or the pass-rate definition, the main claim is unverifiable.\n\nThe other real soft spot is the sub-task benchmark overlap. Section 3.1 builds the Guideline Library from the same 584 papers that Section 4 uses to construct the index and figure subtasks. The paper only vouches for independence of the end-to-end data. That means the 71.86% index and 79.52% figure numbers could partly reflect retrieval of benchmark source material rather than general reasoning. The authors need to either build the subtasks from a held-out corpus or show that knowledge-base entries do not overlap with the benchmark's papers.\n\nThe paper also ships no code, data, or confidence intervals, and the ablation study is under-powered, though the ablation directions look sensible. These are fixable.\n\nOverall: this deserves a serious referee, but not acceptance as written. I'd ask the authors to reconcile the pass-rate arithmetic, address the corpus overlap, and release artifacts. If they can do that, the system and benchmark could be a useful contribution to the meteorological-agent line of work.","headline":"A well-specified meteorological agent with a useful benchmark, but the headline pass rate is arithmetically incompatible with its own Table 1, and the subtask benchmark shares its source corpus with the knowledge base.","tokens_in":16973,"tokens_out":3867,"would_cite":false,"duration_ms":35416,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper presents HVR-Met, a multi-agent system that automates extreme weather diagnosis through a hypothesis-verification-replanning loop, achieving reported pass rates of 71.86% for index computation, 79.52% for figure generation, and 8","keywords":["extreme weather diagnosis","multi-agent system","hypothesis-verification-replanning","meteorological knowledge base","benchmark","index computation","figure generation","diagnostic reporting"],"falsifier":"Construct a fresh benchmark from extreme-weather analyses published after the knowledge base was frozen, ensuring zero overlap with the 584-paper corpus, and check whether index, figure, and report pass rates remain near 71.86%, 79.52%, and 85%; a large drop would show the original numbers overstate generalization.","tokens_in":16081,"feed_emoji":"🌩️","tokens_out":2240,"duration_ms":25768,"temperature":0.7,"pith_summary":"The paper tries to show that an AI agent system can perform operational extreme weather diagnosis end to end, not just forecast. It argues that the missing piece is professional knowledge plus an iterative reasoning loop that revises hypotheses when evidence does not match. The system, HVR-Met, combines a domain guideline library built from 584 expert papers with a closed-loop mechanism that generates, verifies, and replans diagnostic pathways. The authors claim strong pass rates on a new benchmark of index computation, figure generation, and report writing, suggesting that routine diagnostic work could be delegated to agents while forecasters supervise.","feed_headline":"System diagnoses extreme weather with 85% report pass rate","feed_subtitle":"An agentic loop replans when evidence fails, turning 584 expert papers into automated forecaster support.","key_machinery":"The core mechanism is the Hypothesis–Verification–Replanning (HVR) closed loop: after an initial physical hypothesis is formed from detected anomalies, the system computes indices and renders figures to verify it, then compares evidence against expectations; if the evidence fails, it rejects the hypothesis, updates memory, and queries the guideline library for an alternative causal mechanism, repeating until a consistent explanation is found. The second key component is the Guideline Library, a structured knowledge base of diagnostic indices and figure types per weather type, extracted semi-automatically from 584 papers and verified by five senior forecasters.","core_discovery":"The central claim is that a multi-agent framework with a 'Hypothesis–Verification–Replanning' loop can emulate the cognitive workflow of expert forecasters and produce professional-grade extreme weather diagnoses. The system decomposes a query into tasks, retrieves relevant diagnostic templates from a knowledge base of 584 papers, computes indices and plots figures, checks physical alignment, and replans when evidence contradicts the hypothesis. Across 100 end-to-end events and 250 atomic subtasks, the system achieves 71.86% pass rate on index computation, 79.52% on figure generation, and 85% on final diagnostic reporting, with ablation studies showing that removing any core agent or the gui","pith_inferences":["The same architecture could generalize beyond meteorology to other high-stakes diagnostic fields (e.g., climate impact assessment, hydrology, or even medical triage) where expert knowledge bases and iterative hypothesis testing are available.","The reported pass rates might overstate generalizability because the benchmark and the knowledge base draw from the same 584-paper corpus; a truly held-out corpus of newer or differently sourced papers would test whether the system reasons or retrieves.","The replanning loop's benefit likely depends on the quality of the verification signals; if the evidence checker is noisy, the loop could falsely reject valid hypotheses, suggesting that improving the VLM-based verification would yield outsized gains.","A practical extension would be a live deployment with forecaster feedback, measuring how often the agent's final report survives expert review without modification."],"forward_implications":["If the reported pass rates hold, operational forecasters could delegate routine diagnostic steps to the agent, reducing manual workload and human error in time-sensitive extreme weather events.","The HVR loop's iterative replanning can be reused in other scientific domains where a hypothesis must be tested against heterogeneous data and tools.","The atomic-level benchmark enables fine-grained evaluation of separate diagnostic capabilities, potentially becoming a standard testbed for weather AI systems.","The ablation results show that domain knowledge integration and closed-loop verification are essential for reliable agentic diagnosis, not optional enhancements."],"fun_headline_variants":["Replan when wrong: agentic loop hits 85% weather diagnosis accuracy","Hypothesis-verification-replanning loop yields 85% extreme weather accuracy","Expert-guided agent replans to hit 85% on extreme weather diagnostics","From 584 papers to 85% pass rate: HVR loop for weather diagnosis","Closed-loop reasoning boosts extreme weather diagnostics to 85%"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The benchmark and the knowledge base both draw on the same 584-paper corpus, so the reported pass rates may partly reflect retrieval of known material rather than independent diagnostic reasoning.","fun_headline_variants_meta":{"raw":{"variants":["Replan when wrong: agentic loop hits 85% weather diagnosis accuracy","Hypothesis-verification-replanning loop yields 85% extreme weather accuracy","Expert-guided agent replans to hit 85% on extreme weather diagnostics","From 584 papers to 85% pass rate: HVR loop for weather diagnosis","Closed-loop reasoning boosts extreme weather diagnostics to 85%"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000995,"raw_usage":{"total_tokens":4024,"prompt_tokens":692,"completion_tokens":3332,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":436,"completion_tokens_details":{"reasoning_tokens":3233}},"tokens_in":436,"tokens_out":3332,"duration_ms":21829,"temperature":1.0,"reasoning_tokens":3233,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-02T19:42:07.929126+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Construct a fresh benchmark from extreme-weather analyses published after the knowledge base was frozen, ensuring zero overlap with the 584-paper corpus, and check whether index, figure, and report pass rates remain near 71.86%, 79.52%, and 85%; a large drop would show the original numbers overstate generalization.","supporting_citations":[],"review_version":1}