{"id":"39aafbc4-b697-45b7-990e-fb697a642162","arxiv_id":"2605.29586","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Introduces FinVerBench benchmark from 43 S&P 500 10-K filings showing most LLMs have 95-100% false positives on clean statements and that rounded vs unrounded rendering changes recall from 79% to 100%.","lead":"FinVerBench is a benchmark built from real SEC 10-K filings of 43 companies to test whether LLMs can detect numerical inconsistencies in financial statements using four error types. A smart generalist might read it to understand why current LLMs produce high false positives on clean data and how benchmark design choices like number rounding change measured performance.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Metrics exclude underdetermined instances, so evidence does not test 'incomplete observability' component of the central claim","rationale":"The reader's weakest assumption already flags the 105-instance observable subset as potentially unstable for cross-variant comparisons. The concern above is a more targeted version of the same selection issue: the subset definition systematically removes the incomplete-observability regime referenced in the strongest claim. This is therefore a refinement rather than an unrelated objection, warranting 'partial' agreement. The overall UNVERDICTED verdict is unaffected because the abstract-only review already noted insufficient access to methods and data details needed to assess such selection effects.","tokens_in":1774,"tokens_out":410,"duration_ms":21973,"concrete_test":"Recompute all binary metrics on the full set of 108 instances (including the previously excluded underdetermined positives). For those instances, score a response as correct only if the model explicitly flags the case as underdetermined rather than returning a false positive or negative. If the single low-FPR model continues to show near-zero FPR while correctly identifying underdetermined cases, the 'incomplete observability' clause gains support; otherwise the claim requires qualification to observable cases only.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim states that the results support financial statement verification as 'calibrated judgment under incomplete observability' (in addition to prompt-induced assumptions and realistic rendering). However, the paper explicitly computes all binary metrics only on the 105-instance observable diagnostic subset after excluding 'underdetermined positive instances whose perturbed line item is not rendered.' By design, this removes precisely the cases in which observability of the injected error is incomplete. The reported findings (high FPR on clean statements for most models; recall drop from 100% to 79% on the rounded variant) therefore address sensitivity to rendering and prompt effects within fully observable error cases, but supply no direct data on model behavior when the relevant line item is absent from the input.","agreement_with_reader":"partial"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces FinVerBench, a benchmark constructed from SEC 10-K XBRL filings of 43 S&P 500 companies, with a four-category error taxonomy (arithmetic, cross-statement linkage, year-over-year, magnitude). It evaluates 15 contemporary LLMs (14 complete runs) on determining numerical consistency of financial statements. All binary metrics are computed on a 105-instance observable diagnostic subset (43 clean, 62 error-injected) after excluding underdetermined positive instances. Results show nine of fourteen runs with 95-100% false positives on clean statements under the guided-checklist prompt on the unrounded variant; one run achieves 0% FPR. On a rounded rendering variant, the best model's recall drops to 79% (from 100%) while maintaining 0% FPR. The authors conclude that the results support a construct-validity view: financial statement verification requires calibrated judgment under incomplete observability, prompt-induced assumptions, and realistic numerical rendering, rather than mere arithmetic detection. The benchmark and code are released publicly.","tokens_in":1933,"tokens_out":684,"duration_ms":14795,"significance":"If the empirical patterns hold, the work supplies concrete, reproducible evidence that LLM performance on financial verification is highly sensitive to prompt design and data rendering choices, with most models exhibiting near-ceiling false positives on clean statements. The public benchmark enables direct replication and extension. The distinction between unrounded and rounded variants provides a falsifiable demonstration of how numerical presentation affects measured recall. These elements strengthen the paper's contribution to evaluation methodology in applied LLM domains.","major_comments":[{"comment":"Abstract: The central construct-validity claim states that the results demonstrate financial statement verification as 'calibrated judgment under incomplete observability' (in addition to prompt effects and rendering). However, the paper explicitly restricts all binary metrics to the 105-instance observable diagnostic subset after excluding 'underdetermined positive instances whose perturbed line item is not rendered.' This design choice removes precisely the cases needed to test behavior under incomplete observability, so the reported FPR and recall differences address only fully observable error cases.","section":"Abstract"},{"comment":"Abstract: The conclusion that verification 'is not merely arithmetic detection' rests on the high FPR observed on clean statements and the recall drop under rounding. Yet the 105-instance subset size (derived from 43 companies) and the post-exclusion filtering are not accompanied by any statistical assessment of variability or sensitivity to the exclusion rule, leaving the load-bearing claim that these patterns generalize beyond the filtered observable cases under-supported.","section":"Abstract"}],"minor_comments":[{"comment":"Abstract: The exclusion of the Gemini 2.5 Pro run (40/108 gateway failures) is noted but not discussed in terms of whether the remaining 14 runs are representative or whether failure modes correlate with model scale or provider.","section":"Abstract"},{"comment":"Abstract: The phrase 'one run achieves 0% observed false positives' would benefit from explicit identification of which model and prompt variant produced this result, to allow readers to assess whether it is an outlier or a replicable configuration.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the detailed and constructive comments. We respond point-by-point to the major comments and indicate where revisions will be made to improve clarity and precision.","responses":[{"response":"We agree that all reported binary metrics are computed exclusively on the 105-instance observable diagnostic subset after excluding underdetermined positive instances, as stated in the abstract. This exclusion is required to maintain definitive ground-truth labels, since underdetermined cases lack sufficient rendered information for a verifiable determination. The interpretive claim regarding 'calibrated judgment under incomplete observability' is motivated by the real-world prevalence of such cases and is supported indirectly by the observed sensitivity to rendering choices and prompt assumptions. However, we acknowledge that the metrics themselves do not directly evaluate behavior on incomplete-observability instances. We will revise the abstract to distinguish more clearly between the empirical results on observable cases and the broader construct-validity interpretation.","revision_made":"partial","referee_comment":"[Abstract] Abstract: The central construct-validity claim states that the results demonstrate financial statement verification as 'calibrated judgment under incomplete observability' (in addition to prompt effects and rendering). However, the paper explicitly restricts all binary metrics to the 105-instance observable diagnostic subset after excluding 'underdetermined positive instances whose perturbed line item is not rendered.' This design choice removes precisely the cases needed to test behavior under incomplete observability, so the reported FPR and recall differences address only fully observable error cases."},{"response":"The 105-instance subset from 43 companies is modest, and the manuscript does not include formal statistical assessments such as bootstrap confidence intervals, variability estimates, or sensitivity analyses to the exclusion rule. This is a valid observation and a limitation of the current work. We will add an explicit statement in the revised manuscript acknowledging the sample size and the absence of such statistical support, while noting that the consistent patterns across 14 model runs provide qualitative corroboration. Stronger statistical characterization would require a larger benchmark and is left for future extensions.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The conclusion that verification 'is not merely arithmetic detection' rests on the high FPR observed on clean statements and the recall drop under rounding. Yet the 105-instance subset size (derived from 43 companies) and the post-exclusion filtering are not accompanied by any statistical assessment of variability or sensitivity to the exclusion rule, leaving the load-bearing claim that these patterns generalize beyond the filtered observable cases under-supported."}],"tokens_in":1612,"tokens_out":532,"duration_ms":23238,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper introduces FinVerBench, a benchmark drawn from real XBRL filings of 43 S&P 500 companies, and reports that nine of fourteen LLM runs hit 95-100% false positives on clean statements while one run hit zero. It also shows that switching to rounded numbers drops recall from 100% to 79% for the best model. Those are the usable findings.\n\nThe work does a few things right. It starts from actual filings rather than synthetic data, defines error categories that match real filing issues, and releases the benchmark plus code. The decision to report the exact count of excluded instances and the effect of the guided prompt is transparent.\n\nThe main soft spot is the interpretation. The abstract concludes that financial statement verification requires calibrated judgment under incomplete observability, prompt effects, and rendering. Yet every binary metric is computed only on the 105-instance subset after dropping underdetermined positives where the perturbed line item is not rendered. That removes exactly the cases needed to test incomplete observability, so the data does not support that part of the claim. The rendering and false-positive results stand without it.\n\nA reader working on LLM evaluation for domain tasks or on financial auditing tools would get value from the benchmark and the reported rates. The paper is coherent on its own terms and the methods are described enough to be checked. It deserves peer review because the empirical observations are specific and the benchmark itself is new, even if the broader framing needs adjustment.","headline":"FinVerBench gives concrete numbers on high false positives and rendering effects in LLM financial checks, but the claim about incomplete observability rests on cases the authors excluded from the metrics.","tokens_in":2389,"tokens_out":377,"would_cite":false,"duration_ms":16575,"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":"Financial statement verification requires calibrated judgment under incomplete observability and realistic rendering rather than arithmetic detection alone.","keywords":["financial statement verification","LLM evaluation","construct validity","error taxonomy","numerical consistency","false positive rates","rendering effects","XBRL data"],"falsifier":"Running the same models on a fresh rounded rendering of the full set and finding that the zero-false-positive model still reaches near-100% recall while others remain high-false-positive would support the claim; if all models instead show identical behavior across renderings, the distinction between arithmetic detection and calibrated judgment would not hold.","tokens_in":2665,"feed_emoji":"📊","tokens_out":675,"duration_ms":17400,"temperature":0.7,"pith_summary":"The paper builds FinVerBench from real SEC filings of 43 companies and tests LLMs on detecting numerical inconsistencies using an error taxonomy of arithmetic, linkage, year-over-year, and magnitude issues. Evaluations on 14 models show that nine produce 95-100% false positives on clean statements under the guided prompt, while one achieves zero observed false positives; switching to rounded numbers drops that model's recall from 100% to 79% with the same zero false-positive rate. The central finding is a construct-validity result: the task involves prompt-induced assumptions and how numbers appear, not simple detection. This matters because it shows why many LLM evaluations on financial consistency may not measure stable capability.","feed_headline":"LLMs flag most clean financial statements as inconsistent","feed_subtitle":"Benchmark shows verification needs calibrated judgment under missing data and rounded numbers, with one model at zero false positives.","key_machinery":"The observable diagnostic subset of 105 instances (43 clean, 62 error-injected) after dropping underdetermined positives, paired with the four-category error taxonomy and controlled rendering variants.","core_discovery":"FinVerBench shows that on the 105-instance observable diagnostic subset under the original guided-checklist prompt and unrounded rendering, nine of fourteen complete LLM runs yield 95-100% false positives on the 43 clean statements while one run records 0% observed false positives; the same calibrated run maintains 0% false positives but drops to 79.0% recall on the realistic rounded variant of the subset, whereas it reached 100.0% recall on the unrounded version. These patterns support the claim that verification is calibrated judgment under incomplete observability, prompt-induced assumptions, and realistic numerical rendering rather than a final performance leaderboard.","pith_inferences":["The same distinction between detection and judgment under missing information may apply to LLM checks on other numerical datasets such as scientific measurements or regulatory reports.","Benchmarks could routinely include multiple rendering variants to expose sensitivity to presentation.","The exclusion rule for underdetermined positives could be tested by varying how much context is hidden in future versions of the benchmark."],"forward_implications":["Nine of fourteen LLM runs produce 95-100% false positives on clean statements.","One run achieves 0% observed false positives with 100% recall on unrounded data.","The same run drops to 79% recall on the rounded variant while keeping 0% false positives.","Rendering choices materially change measured recall.","The results favor a construct-validity reading over a leaderboard ranking."],"fun_headline_variants":["Most LLMs mislabel clean financial statements as inconsistent","High false positives plague LLM financial statement checks","One LLM records zero false positives on clean statements","Rounded numbers cut LLM recall while keeping zero FPR"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The 105-instance observable diagnostic subset after exclusions and the guided-checklist prompt together provide a stable basis for comparing LLM performance across rendering variants.","fun_headline_variants_meta":{"raw":{"variants":["Most LLMs mislabel clean financial statements as inconsistent","High false positives plague LLM financial statement checks","One LLM records zero false positives on clean statements","Rounded numbers cut LLM recall while keeping zero FPR"]},"model":"grok-4.3","cost_usd":0.004584,"raw_usage":{"total_tokens":2325,"prompt_tokens":768,"num_sources_used":0,"completion_tokens":58,"cost_in_usd_ticks":45837000,"prompt_tokens_details":{"text_tokens":768,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1499,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":768,"tokens_out":58,"duration_ms":11327,"temperature":1.0,"reasoning_tokens":1499,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T07:43:05.575654+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same models on a fresh rounded rendering of the full set and finding that the zero-false-positive model still reaches near-100% recall while others remain high-false-positive would support the claim; if all models instead show identical behavior across renderings, the distinction between arithmetic detection and calibrated judgment would not hold.","supporting_citations":[],"review_version":1}