{"id":"18bdd8a6-de9e-4464-88c3-e4312eeea8ed","arxiv_id":"2607.19793","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A six-category taxonomy of silent failures in multimodal agentic search shows answer-level accuracy overestimates trajectory-grounded correctness across four frontier models.","lead":"This paper identifies six ways multimodal search agents can silently fail even when their final answer is correct, and builds a judge-based pipeline to measure them. The authors find that standard answer-level accuracy overestimates true trajectory-level correctness for four frontier models, and that failures shift rather than disappear as models or tools improve.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"TCR gap is partly tautological and rests on unvalidated LLM failure labels; cross-family κ for the categories driving the drop (mod-sc 0.031, or-ld 0.301) is too low to support the claim as stated.","rationale":"The reader's weakest assumption—that the LLM judge's binary failure labels are reliable measurements—is precisely the load-bearing point. My analysis confirms this: the direction of the TCR gap is definitional, so the claim's empirical weight depends on the validity of the failure labels. The paper's Table 4 provides direct evidence of instability for mod-sc and or-ld, the two categories most responsible for the headline gaps and for the cross-model 'failure shift' narrative. The proposed human-annotation check is a natural, feasible test that would either validate the instrument or force a reinterpretation. I considered the blank-image stress test as a potential independent support, but it only demonstrates that most tasks require the image; it does not validate the six-category taxonomy or the TCR definition. I also considered sample-size/CI issues, but they are secondary: even with narrow CIs, the effect could be an artifact of judge bias. Because the paper itself discloses the low inter-judge agreement and the authors could reasonably address the concern with gold labels, the existing CONDITIONAL verdict remains appropriate; no change is needed. The tone of the central claim should be tempered only if the proposed test fails.","tokens_in":7526,"tokens_out":4454,"duration_ms":47414,"concrete_test":"Select the 100 trajectories already used for cross-judge validation (or a new stratified sample of ~150 from the 800), have 2–3 human annotators independently label all six failure categories using the paper's rubric, and construct majority-vote gold labels. Then report per-category human–judge κ and recompute the Table 2 Accuracy−TCR gap using only human-majority labels on this sample. If the gap remains for all four models and the dominant categories (especially mod-sc and or-ld) have human κ ≥ 0.6, the central claim is supported. If the gap shrinks materially or the categories are not reliably reproducible by humans, the claim should be reframed as judge-dependent and the effect size should be reported per judge.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim is that surface accuracy consistently overestimates true trajectory-level correctness, quantified by the Accuracy minus TCR gap in Table 2. Because TCR is defined as answer-correct AND no silent-failure flag, TCR ≤ Accuracy is true by construction; the only substantive content is the magnitude and categorical interpretation of the drop. That content rests entirely on the primary LLM judge's six binary failure labels. The paper's own cross-judge data show these labels are not stable for the exact categories that drive the largest drops: mod-sc has cross-family κ=0.031 with P_o=25%, meaning the two judges are essentially independent in how they apply the label, and or-ld has κ=0.301 with P_o=66% (Table 4). Gemini 3.1, which accounts for the largest Acc→TCR drop (61.3→54.0), is described as having the highest mod-sc rate; if mod-sc labeling is largely idiosyncratic, that drop is not a measured fact about the model but an artifact of the primary judge's decision boundary. The same concern extends to the tool-ablation shifts in mod-sc and or-ld (Table 5), which are also computed from these unstable labels. Answer_correct is stable (κ≈0.82–0.92), so answer-level accuracy is reliable; the problem is specifically the trajectory-level component. No human gold labels, no validator-specific TCR computations, and no per-category confidence intervals are reported, so there is currently no evidence that the failure labels measure a stable property rather than a prompt-dependent bias.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a six-category taxonomy of 'silent failures' in multimodal agentic search trajectories (modality shortcut, phantom grounding, wrong-evidence-right-answer, over-retrieval laundering, cross-modal contradiction, provenance hallucination) and a diagnostic pipeline in which an LLM judge labels each trajectory for answer correctness and for each failure category. Using 800 trajectories from four frontier multimodal models on MMSearch-Plus, the authors report that answer-level accuracy consistently overestimates a 'true correctness rate' (TCR) that requires a correct answer and no failure flag. They also report cross-judge agreement, a blank-image stress test, and a tool-ablation study, concluding that silent failures are capability-dependent and often shift rather than disappear.","tokens_in":7918,"tokens_out":4717,"duration_ms":48977,"significance":"If the central claim were established, the paper would make a useful point: answer-only evaluation can conceal trajectory-level grounding problems, and stronger models may not monotonically reduce such problems. The paper's strengths include a clearly defined taxonomy, a unified scaffold for trajectory collection, an explicit cross-judge validation design, a blank-image stress test, and a controlled tool ablation. The cross-judge analysis is unusually honest in reporting which labels are stable. However, the central quantitative claim is currently not supported because the TCR gap is computed entirely from fine-grained failure labels that the paper's own validation shows to be highly judge-dependent for the very categories driving the main effects.","major_comments":[{"comment":"The headline claim that 'surface accuracy consistently overestimates true trajectory-level correctness' rests on the primary LLM judge's six binary failure labels. The paper's own cross-judge validation shows that the two categories with the largest influence on the drop — mod-sc and or-ld — have cross-family Cohen's κ of 0.031 and 0.301, with observed agreement 25% and 66%, respectively. The largest Acc→TCR drop (Gemini 3.1: 61.3→54.0) is attributed to high mod-sc, and the tool-ablation shifts (Table 5) also involve these unstable labels. Since no human gold labels, no validator-specific TCR, and no per-category confidence intervals are reported, the measured TCR gap may be an artifact of the primary judge's idiosyncratic decision boundary rather than a stable property of the models. The authors should report TCR computed with each validator separately, TCR excluding the most unstable c","section":"§3.2, Table 2; §3.3, Table 4"},{"comment":"Even accepting the primary judge's labels, the Acc−TCR differences are all within the listed 95% Wilson confidence intervals (e.g., Gemini 3.1: Acc 61.3 [52.5, 69.4] vs TCR 54.0 [45.3, 62.6]). Because TCR is defined as answer-correct AND no silent-failure flag, TCR≤Accuracy holds by construction; the direction of the difference is therefore not surprising and the sign alone cannot be treated as evidence. The substantive content is the magnitude and its variation across models, but no significance test or effect-size uncertainty is provided. The claim that accuracy 'consistently overestimates' TCR should be accompanied by a statistical comparison, e.g., paired tests or confidence intervals on the differences.","section":"§3.2, Table 2"},{"comment":"The LLM judge and the same-family/cross-family validators are never identified. The paper refers only to 'an LLM judge' and 'a same-family judge' and 'a cross-family judge,' without naming the models, the exact rubric, the prompt, the temperature, or the threshold used to turn the rubric into binary labels. This makes the central measurement irreproducible and prevents readers from assessing whether the primary judge is a reasonable choice. The authors should specify all judge models and release the exact prompts and rubric alongside the code/data.","section":"§2.3"},{"comment":"The tool-ablation conclusions — e.g., 'mod-sc and or-ld decrease' or 'pht-gr and cm-ct increase' — are based on the same unstable failure labels. With cross-family κ for mod-sc and or-ld near zero or low, the reported Δ values for these categories are not credible as measurements of tool-effects. The ablation analysis should report validator-specific deltas, per-category intervals, or at minimum restrict the shift interpretation to the categories with acceptable agreement (pht-gr, cm-ct, and possibly prv-hl/we-ra with prevalence caveats).","section":"§3.3, Table 5"}],"minor_comments":[{"comment":"The table uses 'v1' and 'v2' without defining them in the caption. State explicitly that v1 = without reverse_image_search and v2 = with reverse_image_search.","section":"Table 5"},{"comment":"The term 'true correctness rate' implies a ground truth. Since the rate is defined by an LLM judge's labels, consider calling it 'judge-assessed trajectory correctness' or 'failure-free correctness rate' to avoid overclaiming.","section":"Abstract / §1"},{"comment":"The sampling details of 'stratified sampling' from MMSearch-Plus are minimal. Report the strata (task categories, difficulty levels) and the distribution of the 200 sampled tasks, as well as the number of trajectories per model that reached each terminal state in the raw set.","section":"§3.1"},{"comment":"The figure lacks error bars or confidence intervals. Given the small committed subset sizes and the judge instability documented in Table 4, adding per-category Wilson intervals would help readers gauge the reliability of the failure-rate differences.","section":"Figure 2"},{"comment":"For we-ra, κ is undefined because of near-zero prevalence; the paper should state this explicitly rather than reporting '–' without explanation, and should note that low-prevalence categories require a different agreement metric (e.g., percent agreement or F1).","section":"§3.3, Table 4"},{"comment":"There are several minor typographical issues, e.g., missing spaces in 'MultimodalAgenticSearch' in the header of Figure 1 and inconsistent capitalization in category names (e.g., 'ModalityShortcut' vs 'mod-sc'). A careful proofread is recommended.","section":"Minor typography"}],"recommendation":"major_revision","confidential_remarks":"The paper is honest about its judge-dependence, which is a strength, but the main empirical claim is currently underwritten by unstable labels. The authors have the data to fix this: reporting validator-specific TCRs, restricting the headline analysis to categories with acceptable cross-judge agreement, and adding human-labeled spot checks would substantially strengthen the paper. If the revised analysis no longer shows a consistent gap, the central claim may need to be softened to a purely conceptual contribution."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper gives the field something real to work with: a named taxonomy of silent failures in multimodal agentic search, with categories like over-retrieval laundering and wrong-evidence-right-answer that genuinely name under-described trajectory failure modes. The blank-image stress test is the most convincing piece of evidence — 206 of 208 answer-correct trajectories collapse when the image is removed, which independently shows that the model is usually relying on the visual input. That is a nice, cheap, falsifiable check. Also, the authors are honest about judge instability in Table 4; they don't hide the low kappas.\n\nThe soft spot is exactly where the reader's stress-test puts it. TCR is defined as answer-correct AND no silent-failure flag, so TCR ≤ Accuracy is true by definition. The substantive content — the size of the gap, the claim of consistent overestimation, the failure-shift narrative — all rests on the primary LLM judge's six binary labels. Those labels are not stable for the categories doing the heaviest lifting. mod-sc has cross-family κ=0.031 with Po=25%, meaning the judges are essentially using different criteria. or-ld is barely better at κ=0.301. No human gold labels are provided, and there are no per-category confidence intervals or validator-specific TCR computations. So the 0.6–7.3 point drops in Table 2 are not established as measured facts about the models; they are at least partly artifacts of one judge's decision boundary.\n\nThat said, the paper's central direction probably survives. The failure taxonomy is plausible, the examples in Figure 1 are concrete, and the blank-image test gives an independent anchor for the claim that answer-level accuracy misses something. The Wilson CIs in Table 2 are wide and overlap between Accuracy and TCR for every model, so the 'consistently overestimates' phrasing is stronger than the data support. The tool-ablation deltas in Table 5 come without any variance estimate and are computed from the same unstable labels. The committed-subset selection also introduces a possible confound when comparing models with very different refusal/exhaustion rates.\n\nWho gets value from this? Anyone building multimodal agent scaffolds, especially those doing LLM-as-judge evaluation of trajectories. The taxonomy will be reused even if the numbers are not. The paper deserves a serious referee: it makes a methodological point that should be tested with human-labelled gold data, a fixed primary judge, and a properly powered analysis. I would not desk-reject it, but I would not accept the headline claim as-is.","headline":"Useful taxonomy with a load-bearing judge-reliability problem: the direction of the TCR gap is plausible, but the size and category shifts are not yet supported by the reported statistics.","tokens_in":8390,"tokens_out":1555,"would_cite":true,"duration_ms":17846,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Final-answer accuracy overestimates how reliably multimodal search agents are grounded, and silent failures shift rather than vanish as models improve.","keywords":["multimodal evaluation","agentic search","silent failure","faithfulness","LLM-as-judge","trajectory-level diagnosis","taxonomy","true correctness rate"],"falsifier":"Re-label the 800 trajectories (or a random subset) with a different LLM judge or, better, with human annotators following the same rubric. If the true correctness rate is not consistently below accuracy, or if the direction of the gap reverses, the central claim is refuted. Specifically, the 208 answer-correct trajectories could be shown to have near-zero silent-failure flags under human annotation, which would make the overestimation effect disappear.","tokens_in":7398,"feed_emoji":"🔍","tokens_out":5814,"duration_ms":51355,"temperature":0.7,"pith_summary":"This paper argues that evaluating multimodal search agents by final-answer accuracy alone is misleading, because a visibly correct answer can be produced through unsupported or fabricated reasoning. The authors introduce a six-category taxonomy of silent failures—modality shortcut, phantom grounding, wrong-evidence-right-answer, over-retrieval laundering, cross-modal contradiction, and provenance hallucination—and build a trajectory-level diagnostic pipeline that labels every agent run for both answer correctness and these failure modes. Running four frontier multimodal models on 800 search trajectories under a common scaffold, the paper shows that the true correctness rate, which requires a correct answer and no silent-failure flag, is consistently lower than surface accuracy across all models. It also finds that stronger models and better tools do not eliminate silent failures but shift their distribution to other stages of the search process. If correct, this work implies that answer-only leaderboards can certify systems that are not actually reliable.","feed_headline":"Answer accuracy hides six silent search failures","feed_subtitle":"A trajectory-level test shows stronger models shift failures instead of removing them.","key_machinery":"At the center of the method is a structured diagnostic record for each trajectory: d(τ) = {c, f1, ..., f6}, where c marks whether the final answer is correct and each f_i marks whether one of the six silent-failure categories is present. The labels come from an LLM judge given the question, the image, the reference answer, and the full trajectory, following a rubric that asks for justification and evidence steps. From these records the paper defines TCR as the fraction of trajectories that are both answer-correct and free of every failure flag. The taxonomy itself is stage-resolved—before retrieval (modality shortcut), during retrieval (over-retrieval laundering), and after retrieval (phanto","core_discovery":"The paper's central claim is that answer correctness and trajectory-grounded correctness are distinct properties, and that current evaluation practice conflates them. Using a rubric-guided LLM judge over full trajectories, the authors report that the true correctness rate (TCR)—defined as a correct final answer with no triggered silent-failure category—is lower than accuracy on the committed subset by 0.6 to 7.3 percentage points for all four models. The failure mix is capability-dependent: the model with the highest committed accuracy also shows the largest accuracy-to-TCR drop, driven mainly by modality shortcuts, while the other models fail predominantly after retrieval through phantom gr","pith_inferences":["A direct extension is to measure the taxonomy's external validity by having human annotators label a sample of trajectories; if human labels agree with the judge on the categories driving the TCR gap, the effect sizes would be on firmer ground.","The taxonomy could serve as a reward signal in reinforcement learning for search agents, but the low cross-judge agreement on modality shortcut and over-retrieval laundering means such a reward would be noisy and would need to be combined with a more reliable signal.","The 'shift rather than disappear' finding suggests that a leaderboard could reward models that simply relocate failures; therefore, future evaluations should pair accuracy with a stage-resolved failure profile.","Since answer correctness is stable but failure labels are not, a pragmatic benchmark might measure reliability as answer correctness plus a minimal set of high-agreement failure categories, trading off diagnostic richness for reproducibility."],"forward_implications":["Answer-only accuracy is insufficient for reliability evaluation; benchmarks should adopt trajectory-level diagnostics to avoid certifying superficially correct systems.","Stronger models do not monotonically reduce silent failures; improvements can move failures from one stage to another rather than remove them.","Adding tools such as reverse image search can raise final-answer accuracy while increasing post-retrieval failures like phantom grounding and cross-modal contradiction.","Most answer-correct trajectories depend critically on the input image; image-independent guessing is not what drives the results.","Because answer-correctness judgments are stable across LLM judges but fine-grained failure labels are not, reliability claims should be based on the stable signal or on failure categories with verified agreement."],"fun_headline_variants":["Six silent failures hide behind search accuracy","Search accuracy overestimates true performance by 7%","Better AI models shift silent search failures, not remove them","Accuracy is not enough: silent failures plague agentic search","Trajectory tests expose six silent failures accuracy misses"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The paper's conclusions rest on the assumption that the LLM judge's binary labels for the six silent-failure categories reliably measure trajectory failure; if these labels are inconsistent or incorrect, then the measured accuracy-overestimation gap and the failure-shift narrative would be artifacts of judge noise.","fun_headline_variants_meta":{"raw":{"variants":["Six silent failures hide behind search accuracy","Search accuracy overestimates true performance by 7%","Better AI models shift silent search failures, not remove them","Accuracy is not enough: silent failures plague agentic search","Trajectory tests expose six silent failures accuracy misses"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000192,"raw_usage":{"total_tokens":1154,"prompt_tokens":685,"completion_tokens":469,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":429,"completion_tokens_details":{"reasoning_tokens":393}},"tokens_in":429,"tokens_out":469,"duration_ms":4975,"temperature":1.0,"reasoning_tokens":393,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T11:41:51.001856+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-label the 800 trajectories (or a random subset) with a different LLM judge or, better, with human annotators following the same rubric. If the true correctness rate is not consistently below accuracy, or if the direction of the gap reverses, the central claim is refuted. Specifically, the 208 answer-correct trajectories could be shown to have near-zero silent-failure flags under human annotation, which would make the overestimation effect disappear.","supporting_citations":[],"review_version":1}