{"id":"32a9e0d6-514f-4833-b333-9a2054f46311","arxiv_id":"2608.06532","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"Confidence scores for financial vision-language models are only usable for automated deferral when produced by trained internal probes, and their reliability varies with task, model, and language.","lead":"This paper tests seven ways of measuring how confident financial AI models are in their chart and document answers, using models trained only on ordinary photos and then pointed at financial figures. It finds that only two trained internal probes produce scores reliable enough to set an error budget, and that the best estimator changes with the task and language.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Correctness labels from a single gpt-5-mini judge, validated on only 100 stratified items, underpin all calibration and deferral metrics; systematic label error on uncovered slices (Chinese, true/false, captioning) could change which estimators appear thresholdable.","rationale":"The paper is a well-executed, deployment-oriented evaluation with several strengths: the out-of-distribution protocol (probes trained on GQA, applied to finance) is clean, the model and condition coverage is broad, and the FinChart-Bench null result is honestly reported. The central claim, however, is comparative and absolute: only the trained probes yield scores that can be thresholded into a controlled error rate. This claim is operationalized through ECE and safe yield, both of which are computed against a single-model-judge binary label. If the judge's errors are systematic on any slice not covered by the 100-item audit (which was stratified only toward numeric/OCR and did not cover Chinese or free-form captioning), the measured calibration of the baselines could be substantially better or worse than reported. Because the gap between baselines (ECE 0.25-0.58) and trained probes (ECE 0.04-0.23) is large, it would take a strong and directionally specific bias to flip the ordering, but the audit is too small to rule that out. The threshold-selection issue is real but secondary: it affects the safe-yield point estimates, not the ECE-based calibration comparison. Seed-averaging of probes is a design choice that could inflate their apparent calibration, but the magnitude of the ECE gap suggests it is unlikely to reverse the ordering. The undefined ACC column in Table 1 and the MME-Finance-EN sample-size inconsistency are editorial problems that should be fixed but do not bear on the central claim as directly. On balance, the reader's identification of the judge label quality as the weakest assumption is correct; a targeted larger audit with corrected-label recomputation would settle whether the central claim survives.","tokens_in":16417,"tokens_out":10047,"duration_ms":93319,"concrete_test":"Draw a stratified random sample of 1,000 (model, question, answer) triples per condition (or at least 500 for MME-Finance-ZH), oversampling Chinese, open-ended captioning, true/false, and numerical-calculation items. Have three independent annotators label each as correct/incorrect, take the majority vote as ground truth, and measure judge agreement on each stratum. Recompute ECE, Brier, AUROC, and safe yield with the corrected labels. If the ordering of estimators by ECE changes, or if P(True) or Self-Probing's ECE drops by more than 0.05 while BICR's changes by less, the central claim that only trained probes are thresholdable would need to be revised.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that only BICR and InternalInspector produce thresholdable confidence scores rests entirely on binary correctness labels assigned by a single gpt-5-mini judge (Section 3, 'Correctness labels'). Every ECE, Brier, AUROC, ConfErr, and safe-yield number in Tables 1, 3, and 4 is computed against these labels. The validation is a 100-item audit (94.8% agreement, Cohen's kappa 0.89) stratified only toward numerically-exact and OCR answers. This is too small to bound systematic error on the much larger and more varied evaluation set, which spans five models, two languages, and twelve task types. A judge that is, say, systematically lenient on high-confidence multiple-choice or true/false answers, or unreliable on Chinese-language outputs, would differentially deflate the measured ECE of overconfident baselines like P(True) and Self-Probing, narrowing the gap the paper's headline claim depends on. The 100-item audit does not cover these slices, and the confidence interval around 94.8% still admits a nontrivial error rate. The paper's own limitation statement concedes the judge is 'imperfect for numerically exact answers,' which is exactly the slice the audit was designed to cover, yet the audit cannot validate the full distribution.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper evaluates seven confidence estimators for financial vision-language models: three inference-only baselines (P(True), Self-Probing, Prompt Ensemble) and four trained internal probes (SAPLMA, CCPS, InternalInspector, BICR). The estimators are trained only on GQA natural-image data and evaluated without adaptation on four financial conditions from FinMME, FinChart-Bench, and the bilingual MME-Finance benchmark, across five open-weight LVLMs. The central claims are threefold: (1) the scarce property is calibration rather than ranking, with the trained probes BICR and InternalInspector being the only estimators whose scores can be thresholded into a controlled error rate; (2) confidence reliability is structured by model and task type, with no estimator dominating more than eight of twenty (model, condition) cells; and (3) safe-yield under bounded-error deferral is set first by base-model competence, with the confidence layer adding yield mostly where the model is weak. The paper also claims that BICR uniquely lowers confidence on figure-invariant answers, separating detected non-grounding from a fluent guess. The protocol is generally clean, and the paper includes several honest null results and limitation statements.","tokens_in":16688,"tokens_out":4928,"duration_ms":49912,"significance":"If the findings hold, this is a useful and timely contribution to selective prediction for financial LVLMs. The out-of-distribution protocol is well designed: all trainable probes are trained only on GQA and evaluated on unseen financial figures, with seed-averaging and per-cell significance tests. The framing in terms of bounded-error deferral, rather than aggregate accuracy, is appropriate for the stated deployment context. The paper also deserves credit for reporting a null result on FinChart-Bench for the grounding diagnostic, for using paired bootstrap and DeLong tests, and for openly acknowledging limitations. The main risk to the significance of the results is that every calibration, discrimination, and safe-yield number depends on binary correctness labels from a single gpt-5-mini judge validated on only 100 items, and the limitation statement itself concedes that the judge is imperfect for numerically exact answers. The BICR grounding finding also requires careful interpretation because BICR's training objective explicitly contrasts real and blanked figures, so part of the observed behavior is definitional rather than an emergent transfer result.","major_comments":[{"comment":"All calibration, discrimination, and safe-yield metrics in Tables 1, 3, and 4 are computed against binary correctness labels assigned by a single gpt-5-mini judge, validated by a 100-item audit stratified toward numerically-exact and OCR answers. That audit does not cover Chinese-language outputs, true/false items, captioning, or open-ended QA, which are substantial parts of the evaluation. Systematic judge error on any of those slices could shift the ECE, Brier, AUROC, and safe-yield values and could change which estimators appear thresholdable, because the headline claim that only BICR and InternalInspector support controlled error rates rests on these labels. The limitation statement's concession that the judge is 'imperfect for numerically exact answers' is also in tension with the audit's claim of no degradation on the numerical/OCR slice. Please report per-slice judge agreement on a larger stratified sample spanning models, languages, and task types, or re-run the key tables under multiple judges or a label-noise model.","section":"Section 3, 'Correctness labels'"},{"comment":"The claim that BICR uniquely lowers its confidence on figure-invariant answers is partly definitional: BICR is trained by contrasting representations under the real figure against those under a blanked figure, so on the first-token-invariant subpopulation, where the model's output does not change when the figure is replaced, the estimator is expected by construction to produce a lower score. The paper's finding that this transfers to financial conditions is still informative, but the text should separate the definitional component from the transfer component. Please report how BICR's confidence correlates with an independent grounding measure, such as full-answer invariance or human judgments of whether the figure was used, and show calibration on that subset against a baseline that does not use blank-image contrast.","section":"Section 5 and Table 4, grounding analysis"},{"comment":"The pooled headline numbers in Table 1 and the safe-yield numbers in Table 3 are presented without confidence intervals or significance tests for the key differences. Some comparisons are close, for example the ECE difference between BICR and InternalInspector on MME-Finance-EN (13.8 vs. 12.4) and the AUROC differences on FinChart-Bench, and the conclusion that only the trained probes are thresholdable depends on the magnitude of these gaps. Please report bootstrap confidence intervals for the main ECE, AUROC, and safe-yield comparisons, or at least for the comparisons that drive the central claims.","section":"Tables 1 and 3"}],"minor_comments":[{"comment":"The DeLong p-values are printed as 0.0e+00 and 3.0e-06; values that underflow should be reported with an inequality such as p < 1e-300 rather than as exactly zero.","section":"Table 2"},{"comment":"The table includes unexplained 'verdict: SUPPORTED' and 'ref' entries; these should be removed or defined in the caption, since they are not part of a standard results table.","section":"Table 4"},{"comment":"The per-task heatmap contains many small two-number cells that are difficult to read at print size; a larger figure or separate panels per task family would improve legibility.","section":"Figure 2"},{"comment":"The sentence describing the 100-item audit says it was 'stratified toward numerically-exact and OCR answers' and that there was 'no degradation on the hard numerical/OCR slice relative to the rest,' but the size of the 'rest' is not stated; please specify how many non-numerical/OCR items were included.","section":"Section 3, 'Correctness labels'"}],"recommendation":"major_revision","confidential_remarks":"This is a competent empirical benchmark with a clean OOD protocol and honest reporting. The main risk is not the experimental design but the correctness labels: a single judge validated on 100 items is a thin foundation for the strong thresholdability claim, especially on the Chinese and true/false slices. I would not reject, but I would require additional label validation or a label-noise sensitivity analysis before accepting. The BICR grounding finding should also be contextualized as partly definitional, with an independent grounding measure if possible."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a solid, well-scoped empirical paper that makes a real point — on financial VQA, inference-only confidence signals rank answers okay but are too overconfident to threshold, while trained internal probes actually support a controlled error rate. The transfer protocol is clean: probes trained only on GQA and evaluated on four financial conditions without adaptation. The per-task reliability map and the bilingual composition-effect analysis are genuinely new. The paper deserves a serious referee; I'd send it out, with conditions.\n\nWhat's actually new: the out-of-distribution comparison of seven estimators across five LVLMs on three financial benchmarks; the finding that task operation (lookup vs captioning vs true/false) organizes separability while calibration tracks method; the bilingual contrast showing apparent language robustness dissolves once you read models individually; and the safe-yield analysis under error budgets. They also report an honest null result on FinChart-Bench for the grounding diagnostic. That's a credible package.\n\nSoft spots, in proportion:\n- The MME-Finance-EN sample size is inconsistent. Section 3 says they use the 892-sample subset from prior work, but the benchmark description and Table 1 list n=1,171 for English. That's a concrete mismatch. If the actual subset is 892, all the EN pooled metrics shift. Needs to be fixed before publication.\n- All calibration and deferral numbers rest on binary correctness labels from a single gpt-5-mini judge, validated on 100 items. The stress-test worry is legitimate: 100 stratified items don't bound systematic error on Chinese, true/false, or captioning slices. That said, the headline gaps are large — BICR's ECE is 8.6 against P(True)'s 34.9 on FinMME, and ConfErr gaps are similar. Label noise would have to be massive and confidence-correlated to flip the main conclusion. Still, a larger audit with slice-wise agreement would make the deployment claims much sturdier.\n- Table 1 has no error bars, and thresholds are selected on the evaluation set for the safe-yield numbers. They disclose the threshold point in limitations, but it means Table 3 is best-case, not expected.\n- BICR is the authors' own estimator, and its grounding-aware objective is exactly what produces the non-grounding backoff. That's not a flaw given the OOD transfer design, but the BICR-specific claim is a property of the estimator's design, not a surprise.\n- No code or data released either, which hurts reproducibility.\n\nNone of this sinks the central argument: the trained probes are calibratable, the inference baselines are not, and reliability is structured, not global. The paper is honest about its limits. I'd accept it for review, ask for the sample-size fix, error bars or bootstrap CIs on headline metrics, and a stronger judge validation. For a reading group, it's a good discussion paper.","headline":"Solid empirical paper on confidence estimation for financial LVLMs; the central calibration-vs-ranking claim holds, but the sample-size inconsistency and single-judge correctness labels need fixing before I'd trust the deployment numbers.","tokens_in":17195,"tokens_out":4060,"would_cite":true,"duration_ms":34420,"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":"Trained internal probes are the only confidence estimators whose scores can be thresholded into a controlled error rate on financial chart and document understanding, and the grounding-aware BICR alone also lowers its confidence when the…","keywords":["confidence estimation","calibration","vision-language models","financial document understanding","selective prediction","selective deferral","grounding","out-of-distribution transfer"],"falsifier":"An independent annotation of several hundred financial VQA answers from the same five models, deliberately oversampling numerical and OCR items, would settle the labeling question: if human labels disagree with the automated judge on a systematic class, every ECE, AUROC, and safe-yield figure shifts. The grounding claim can be tested separately by replacing first-token invariance with full-answer invariance on human-labeled ungrounded answers, and checking whether BICR's confidence drops specifically on those answers.","tokens_in":16259,"feed_emoji":"📊","tokens_out":11115,"duration_ms":84662,"temperature":0.7,"pith_summary":"Confidence scores for financial vision-language models are only operationally useful if they can be thresholded into an error-controlled automation policy, and the paper argues that among seven estimators tested across five models, only two trained internal probes deliver that property. The inference-only baselines—verbalized self-report, self-probing, and prompt ensembles—rank correct above incorrect answers almost as well but are badly overconfident, with calibration error far above what a threshold can tolerate. The trained probes BICR and InternalInspector, transferred zero-shot from natural images to financial charts, tables, and documents, are the only estimators whose scores map to a controlled error rate, and BICR alone lowers its confidence when the model answers without using the figure. Read as deferral under an error budget, how much can be safely automated is set first by the base model's competence, so the strongest models can clear a real share of easy chart questions while nearly nothing survives a strict budget on the hardest document tasks. The paper is a caution to anyone who uses a raw confidence readout to decide whether a financial AI answer can be acted on.","feed_headline":"Financial charts: only trained probes yield thresholdable confidence","feed_subtitle":"Self-reports rank answers but are too overconfident to threshold; BICR and InternalInspector calibrate.","key_machinery":"The argument is carried by two trained internal probes plus the selective-prediction framing. BICR (blind-image contrastive ranking) trains a confidence probe on the contrast between hidden representations under the real figure and under a blanked or deterministic random image, so its score encodes whether the answer used the figure; this is the only estimator here trained with a grounding signal. InternalInspector learns contrastively across attention, feed-forward, and activation states from all layers and supplies the other consistently well-calibrated score. Both are trained only on GQA natural images and applied to finance without adaptation, so the study measures out-of-distribution transfer. The deployment analysis uses bounded selective prediction: a risk-coverage curve is traced for each method and the safe yield is the largest automatable fraction at a 5% or 20% error budget, which is why calibration, not AUROC, is the property that makes a threshold meaningful.","core_discovery":"The paper's central discovery, stated in its own terms, is that the scarce property in confidence estimation for financial LVLMs is calibration, not ranking. Across four financial conditions, the inference-only baselines P(True), Self-Probing, and Prompt Ensemble produce competitive AUROC values (up to $0.774$ on the bilingual document condition) but ECE between $0.25$ and $0.58$, and Self-Probing labels as many as $93.8\\%$ of its own errors with confidence above $0.8$, so no fixed threshold yields a controlled error rate. Trained internal probes, all trained only on natural-image GQA and evaluated on finance with no adaptation, hold the best calibration on every condition—BICR at ECE $8.6$ on FinMME and InternalInspector at $4.3$ on FinChart-Bench—making them the only estimators whose scores can be thresholded into a bounded-error deferral policy. On the subpopulation where replacing the chart with a random image leaves the first generated token unchanged, BICR alone drops its mean confidence sharply ($0.492$ versus $0.721$ for P(True) on FinMME) while remaining well calibrated, separating detected non-grounding from a fluent guess. The paper also finds that safe yield tracks base-model competence ($r = 0.68$), so the confidence layer adds deployable yield chiefly where the model is weak.","pith_inferences":["A direct test of whether the out-of-distribution gap is a lower bound would be to train BICR and InternalInspector on a modest slice of financial VQA data; if calibration improves further, the current transfer-only numbers understate what a deployment should expect from in-domain training.","The first-token-invariance diagnostic could be generalized to full-answer invariance; if the grounding signal holds under that stricter test, BICR-style scores could be used to flag ungrounded open-ended generations, the task family where every estimator studied here is weakest.","The safe-yield framing could be turned into a cost-of-error knob: a bank that assigns a dollar cost to an automated wrong answer can invert the risk-coverage curve to choose the automation threshold explicitly, rather than using a fixed 5% or 20% budget.","The bilingual composition-artifact result suggests that any pooled leaderboard, including the headline tables in this paper, should be read with per-model operating points in hand before deployment; the paper's own pooling shows how aggregation can hide both degradation and reachable yield."],"forward_implications":["A practitioner who gates automation on a confidence threshold should use BICR or InternalInspector, because only these two trained probes map a fixed cutoff to a controlled error rate across all four financial conditions.","Raw verbalized and logit self-reports should not be thresholded for financial deferral: they rank well but are overconfident enough that a cutoff at 0.8 can correspond to error rates far above budget.","Deferral yield is bounded by base-model competence, so confidence layers add automation mostly on weak models; the strongest models on easy chart conditions can clear substantial workload while even the best estimator clears almost nothing on the hardest document and Chinese-language conditions at a strict 5% budget.","Reliability must be reported per model and per task family: no estimator leads more than eight of twenty (model, condition) cells, and pooled bilingual numbers can create an apparent language robustness that disappears within individual models.","On the figure-invariant subpopulation, BICR's low, well-calibrated confidence identifies fluent answers produced without reading the chart, which is the failure mode most dangerous for a financial reviewer."],"supporting_citations":[{"why":"Defines the grounding-aware BICR probe that is the paper's central deployment-ready estimator.","marker":"[17]"},{"why":"Defines InternalInspector, the second probe whose calibration supports thresholding.","marker":"[2]"},{"why":"Supplies the GQA natural-image data on which all trained probes are trained, defining the out-of-distribution transfer.","marker":"[12]"},{"why":"Supplies FinMME, the broad financial multimodal benchmark used as one of the four conditions.","marker":"[22]"},{"why":"Supplies FinChart-Bench, the chart-understanding condition on which free softmax baselines already deploy well.","marker":"[23]"},{"why":"Supplies MME-Finance, the bilingual document condition whose English–Chinese split anchors the language-robustness analysis.","marker":"[7]"},{"why":"Formalizes selective classification and the risk-coverage tradeoff underlying safe yield.","marker":"[6]"},{"why":"Provides the selective-classification machinery used to define coverage and safe yield.","marker":"[8]"},{"why":"Frames deferral under a bounded error budget with a second opinion, the deployment setting studied here.","marker":"[19]"},{"why":"Supplies the P(True) verbalized self-report baseline that is competitive at ranking but miscalibrated.","marker":"[14]"}],"fun_headline_variants":["Calibration, not ranking, is the scarce property for financial LVLMs","Only trained probes calibrate financial chart confidence","Grounding-aware probe separates non-grounding from fluent guess","Deferral yield tracks base-model competence, not confidence"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the single automated judge used to label every answer correct or incorrect is accurate enough on the hard slices—especially numerically exact and OCR answers—to support all the calibration, ranking, and safe-yield numbers; the paper validates the judge on a 100-item audit with 94.8% agreement, which is a thin base for the strongest claims.","fun_headline_variants_meta":{"raw":{"variants":["Calibration, not ranking, is the scarce property for financial LVLMs","Only trained probes calibrate financial chart confidence","Grounding-aware probe separates non-grounding from fluent guess","Deferral yield tracks base-model competence, not confidence"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000599,"raw_usage":{"total_tokens":2910,"prompt_tokens":1166,"completion_tokens":1744,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":782,"completion_tokens_details":{"reasoning_tokens":1676}},"tokens_in":782,"tokens_out":1744,"duration_ms":11126,"temperature":1.0,"reasoning_tokens":1676,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T04:16:24.084448+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"An independent annotation of several hundred financial VQA answers from the same five models, deliberately oversampling numerical and OCR items, would settle the labeling question: if human labels disagree with the automated judge on a systematic class, every ECE, AUROC, and safe-yield figure shifts. The grounding claim can be tested separately by replacing first-token invariance with full-answer invariance on human-labeled ungrounded answers, and checking whether BICR's confidence drops specifically on those answers.","supporting_citations":[{"cited_title":"Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking","cited_arxiv_id":"2605.10893","evidence_quote":"Defines the grounding-aware BICR probe that is the paper's central deployment-ready estimator."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies FinMME, the broad financial multimodal benchmark used as one of the four conditions."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Formalizes selective classification and the risk-coverage tradeoff underlying safe yield."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the selective-classification machinery used to define coverage and safe yield."}],"review_version":1}