{"id":"cb0fe7d9-5161-4f87-ba2a-d4f83e73dcff","arxiv_id":"2607.25891","paper_version":1,"verdict":"CONDITIONAL","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A unified corpus of 957k trial outcomes shows frontier progress is uneven and strict all-pass aggregation obscures capability and can reorder agents.","lead":"MESSIER: a corpus of 957,253 standardized agent-evaluation records across 30 benchmarks, with five-agent runs added on six underrepresented domains, plus analysis showing that all-pass aggregation can hide progress and reorder agents.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The headline counterfactual and enterprise-frontier numbers rest on an unvalidated local HarveyAI-Lab verifier; without official-score comparison they are illustrative, not established.","rationale":"The reader's weakest assumption is right. The paper's most striking empirical result — that strict all-pass aggregation makes measured capability collapse to 0.2% while soft scoring gives 28.8%, and that this reorders agents (ρ=0.667) — is computed from Messier's own HarveyAI-Lab verifier, not from Harvey's official grading. Appendix A.5 describes the verifier as an LLM judge with 33-65 criteria per task and no validation. This matters more than the ECI alignment issue because the ECI ρ=0.81 is a secondary application and its comparison targets are published; the counterfactual and frontier numbers are primary claims and their only source is the unvalidated judge. The paper's Limitations section does not cover this gap; it only disclaims upstream benchmarks. If the judge is even moderately biased, the magnitude and rank-order results change, so the strongest claim currently has an unresolved correctness risk. This does not require rejecting the paper: τ2-bench and TheAgentCompany show the same qualitative aggregation sensitivity, the corpus itself may still be useful, and the authors could fix the gap by validating against official Harvey scores. Since the reader already recommended CONDITIONAL, my read leaves that verdict unchanged.","tokens_in":18787,"tokens_out":4467,"duration_ms":42149,"concrete_test":"Run the official HarveyAI-Lab verifier/scoring code on the same five-agent OpenHands rollouts used for Messier's contributed Harvey runs. Report per-task all-pass rate, mean soft criterion score, and per-agent Spearman between all-pass and soft under the official rubric, and compare with Table 3's Harvey row. Also record the number of official criteria per task vs the 33-65 local criteria. If official all-pass differs materially from 0.002, or the official soft/all-pass rank correlation differs materially from 0.667, the counterfactual result is an artifact of the local judge and should be reported as such. A secondary check: recompute the Figure 1f enterprise frontier with HarveyAI-Lab scored officially; if the 0.57 point shifts, the 'enterprise workflows remain hardest' claim needs qualification.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing assumption is that the HarveyAI-Lab verifier-level outcomes in Messier are faithful to the upstream benchmark's own grading. HarveyAI-Lab is one of six contributed runs (§4, A.5), and its per-criterion outcomes come from a local LLM judge (anthropic/claude-sonnet-4-6) issuing independent binary judgments for 33–65 criteria per task, aggregated by all-pass. The preprint reports no validation of this judge against the official Harvey scoring, despite the Limitations section saying the authors 'largely assume the validity of underlying datasets'; this verifier is not an underlying dataset but a new component. Because Harvey contributes 1,000 of ~2,572 enterprise tasks (39%) and 221k of 960k outcomes, both Table 3's Harvey row (all-pass 0.002 vs soft 0.288; per-agent ρ=0.667) and the enterprise-workflow frontier point 0.57 in Figure 1f inherit whatever bias the local judge has. A judge that fragments official criteria into many subcriteria, or that is systematically stricter, will mechanically depress all-pass rates and inflate soft-vs-all-pass gaps. The qualitative point that aggregation matters is supported by τ2-bench and TheAgentCompany, but the dramatic quantitative headline is not independently established.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"Messier is proposed as a unified corpus of 957,253 standardized trial outcomes spanning 30 benchmarks, 714 agents, 11,891 tasks, and 74,205 verifiers, assembled from public releases plus new five-agent runs on six benchmarks (HarveyAI-Lab, MedAgentBench, DABStep, QCircuitBench, ScienceAgentBench, ReplicationBench) under a fixed OpenHands/Harbor configuration. On this corpus the paper reports three analyses: (i) frontier progress, finding function-calling near saturation, programming fastest-improving, and enterprise workflows most challenging (Figure 1f); (ii) counterfactual rescoring, showing that all-pass aggregation can obscure partial progress and reorder agents (Table 3); and (iii) 1PL IRT capability scales correlating with Epoch ECI at Spearman ρ=0.81 over 75 matched agents (Figure 2), plus a difficulty-prediction experiment (Table 4).","tokens_in":19026,"tokens_out":24930,"duration_ms":216148,"significance":"With the caveat below, the corpus would be a valuable community asset: roughly eight times HAL in task count, verifier-level rather than task-level, with careful per-source license/access tables, deterministic hygiene checks, and modular per-benchmark builders. The ECI alignment is a genuine external anchor: ρ=0.81 over 75 matched agents is meaningful, although it partly validates the reconciliation pipeline given shared source benchmarks. The qualitative aggregation result is also supported by tau2-bench and TheAgentCompany, whose per-criterion outcomes come from public releases. The paper is honest about the difficulty-prediction baseline and includes a thoughtful Limitations section. The central risk in this manuscript, fidelity of the locally constructed HarveyAI-Lab verifier, does land on reading §A.5 and is detailed in Major Comment 1.","major_comments":[{"comment":"The HarveyAI-Lab row rests on a verifier constructed by the authors, not on the upstream benchmark grading. For each of 1,000 tasks, anthropic/claude-sonnet-4-6 issues 33-65 binary criterion judgments (§A.5), so Table 3 all-pass 0.002 vs soft 0.288, near-pass 0.080, and per-agent ρ=0.667 are internal properties of that judge. No validation against official Harvey scores, human labels, or a second judge is reported. The Limitations sentence that the authors largely assume the validity of underlying datasets does not cover a newly constructed judge. Because Harvey supplies 1,000 of 2,572 enterprise tasks and 221k of 957k outcomes, the enterprise-frontier point 0.57 (Figure 1f) inherits this bias. Please validate the judge on a sample against official scoring and re-derive these figures, or explicitly label the Harvey counterfactual as illustrative.","section":"§5.2 / §A.5 / Table 3"},{"comment":"The claim that aggregation artificially alters agent rankings is only weakly supported by the HarveyAI-Lab row: ρ=0.667 is computed across n=5 agents (Table 2), each with one trial per task (§A.5), with no confidence interval or significance test, and most all-pass means cluster near zero, making the estimate tie-dominated. Moreover, with roughly 60 criteria per task and mean criterion pass 0.288, an all-pass rate near zero is close to a mathematical consequence (0.288^60 ≈ 0), so the empirical content of that row is the per-criterion rate of the unvalidated judge (Major Comment 1). The stronger reordering evidence is TheAgentCompany (n=18, ρ=0.981) and tau2-bench (n=7, ρ=0.857). The paper calls for broader validation in §5.2; the abstract should reflect that caution.","section":"§5.2, Table 3"},{"comment":"The per-task frontier is the maximum score over eligible agents, so its expected value grows with the number of eligible agents, which differs by an order of magnitude across the groups compared: function-calling has 108 agents, programming up to 192, while enterprise benchmarks have 5-18 (Table 2). The headline that enterprise workflows remain the most challenging at 0.57 is therefore confounded with agent coverage and, for the 1,000 Harvey tasks, with the unvalidated verifier of Major Comment 1. The §5.1 disclaimer that the analysis is not intended to estimate field-wide progress is appropriate but does not protect the abstract statement. Please add a robustness check on a common agent subset or an explicit coverage adjustment, and show per-benchmark eligible-agent counts alongside the curves.","section":"§5.1, Figure 1f"}],"minor_comments":[{"comment":"BFCL-Multi-Turn is described as matching the agent per-turn method-call trace turn by turn (§A.1) but Table 2 lists one verifier per task (800 verifiers / 800 tasks). Clarify whether trace matching is a composite single verifier; otherwise it may qualify as a multi-verifier all-pass benchmark and should be considered for §5.2.","section":"§A.1 / Table 2"},{"comment":"The dumbbell markers and +Δ annotations are not defined in the caption; state which endpoint is the first versus final observed quarter, and what the delta values are.","section":"Figure 1f"},{"comment":"The z-scored-β baseline value of -0.081 is confusing; state that the baseline predicts the training-fold benchmark mean and can be negative when the target is within-benchmark z-scored difficulty.","section":"Table 4"},{"comment":"Each (agent, task) cell is run once. Add a sentence acknowledging that Table 3 and Figure 1f estimates carry no sampling error and would benefit from repeated trials or interval estimates.","section":"§A.5"},{"comment":"SOC/NAICS classification reports only 2-of-3 voter agreement (88.3%/86.8%); consider validating the labels against a small human-annotated sample, since agreement among the same-family voters is not accuracy.","section":"§A.3"},{"comment":"Extend the validity-assumption sentence to cover newly introduced verifiers (in particular the Harvey judge), and note the single-trial design, so readers can calibrate trust in the contributed runs.","section":"Limitations"},{"comment":"Because many Messier benchmarks overlap Epoch's source pool, ρ=0.81 partly verifies the reconciliation pipeline rather than a fully independent scale; mention this explicitly and, if feasible, add a robustness check excluding shared benchmarks.","section":"§5.3"}],"recommendation":"major_revision","confidential_remarks":"The paper is close to publishable as a dataset and measurement contribution: the ECI alignment and the aggregations on tau2-bench/TheAgentCompany give the corpus external anchors. The decisive question is whether the authors can validate, or explicitly hedge, the HarveyAI-Lab verifier; without that the most visible numbers in the abstract and §5.2 are not established. I could not inspect the anonymized repository during review; the actual release of the code, builders, and derived records should be a condition of acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know before you spend time. First, this is a genuinely useful corpus paper: 957k verifier-level records across 30 benchmarks, per-verifier outcomes, SOC/NAICS labels, and six new runs. If the data ships, it saves researchers a lot of redundant compute. Second, the headline counterfactual result — all-pass giving 0.2% task success on HarveyAI-Lab while soft scoring gives 28.8% — is built on a locally constructed LLM judge that the authors never validated against the original benchmark's official scoring. That number is illustrative, not established.\n\nThe real addition over HAL, BRIDGE, and Agent Psychometrics is the per-verifier granularity at this scale and the occupation/industry slicing. The reconciliation pipeline is thoughtful: normalizing model, scaffold, task, verifier, and aggregation into one schema is hard, and they include external sanity checks — the BRIDGE human-time replication (B.1) and the ECI alignment at ρ=0.81. Those are good signs.\n\nThe biggest soft spot is the Harvey verifier. The limitations section says they largely assume the validity of underlying datasets, but that judge is not an underlying dataset; it's a new component. They report no comparison against Harvey's official scores. Since Harvey supplies 221k of 960k outcomes and about 39% of enterprise tasks, the enterprise-frontier point and the Table 3 Harvey row inherit whatever bias that judge has. The qualitative point that aggregation matters does survive: τ2-bench and TheAgentCompany use original verifiers and show the same pattern, just less dramatically. So the general claim is fine; the dramatic headline is not.\n\nSecond, reproducibility: the dataset URL is anonymous with no commit hash. For a dataset paper, that's a problem for review. The IRT section also deserves more sensitivity analysis around scaffold selection and priors, though they do report 1PL/2PL ordering agreement at 0.98. The difficulty prediction is honestly reported: raw β barely beats a benchmark-mean baseline.\n\nWho this is for: anyone working on agent evaluation or benchmarking meta-analysis. It deserves a serious referee, not a desk reject, because the corpus could be a real resource and the aggregation sensitivity question matters. The referee should demand the data and code, and a validation of the Harvey judge against official scores. I'd probably cite it if the corpus is released, but I wouldn't use the Harvey numbers in the meantime.","headline":"Useful corpus with a real caveat: the HarveyAI-Lab counterfactual numbers rest on an unvalidated local verifier, so treat them as illustrative until the authors verify against official scoring and actually release the data.","tokens_in":19621,"tokens_out":3654,"would_cite":true,"duration_ms":31951,"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":"The paper introduces a unified corpus of 957,253 agent-evaluation records and shows that the choice of aggregation rule—all-pass versus partial credit—can change both measured capability and agent rankings.","keywords":["agent evaluation","benchmark corpus","verifier-level records","aggregation rules","item response theory","capability scaling","counterfactual rescoring","task difficulty prediction"],"falsifier":"Take a multi-verifier benchmark that publicly releases both official task-level scores and individual criterion outcomes, and check whether the paper's rescoring pipeline exactly reproduces the official all-pass scores from the criterion outcomes; a mismatch would indicate the pipeline (or its local judge) diverges from the upstream grader. Concretely, for the six contributed benchmarks, run the upstream benchmarks' original verifiers on the same recorded traces and compare per-criterion outcomes with the local LLM judge's judgments—if they disagree on a large fraction of criteria, the headlin","tokens_in":18593,"feed_emoji":"📊","tokens_out":5804,"duration_ms":47469,"temperature":0.7,"pith_summary":"This paper builds Messier, a corpus of 957,253 trial records spanning 30 benchmarks, 714 agents, 11,891 tasks, and 74,205 verifiers, with each record standardized by model, scaffold, environment, task, verifier, and aggregation rule. Using verifier-level granularity, it argues that a benchmark's aggregation rule is not a neutral reporting choice: on a long-horizon legal-workflow benchmark, the official all-pass rule reports 0.2% task success even though the same trials satisfy 28.8% of criteria on average, and the two scoring methods reorder agents (Spearman ρ = 0.667). The harmonized records also support fitting a 1PL item-response model whose ability rankings match an established cross-benchmark capability index at ρ = 0.81 across 75 matched agents without any new evaluation campaign. A sympathetic reader would care because the corpus makes existing evaluations reusable, auditable, and re-scorable at the criterion level, and because the aggregation-sensitivity results temper how single-number leaderboards should be read.","feed_headline":"All-pass scoring reorders AI agents, 957k-trial corpus finds","feed_subtitle":"On one legal-workflow benchmark, official grading reports 0.2% success where trials pass 28.8% of criteria; granular records reveal it.","key_machinery":"The corpus itself is the central object: a reconciled schema in which every trial is decomposed as (model, scaffold, environment, task, verifier, aggregation rule), with 74,205 verifiers and per-criterion outcomes for multi-verifier tasks. Two mechanisms carry the argument. (1) Counterfactual rescoring: because records preserve each verifier's binary outcome, any aggregation rule—all-pass, majority-pass, or soft mean—can be applied post hoc to the same trials, revealing how much of measured capability is an artifact of the rule. (2) A 1PL Rasch (item-response) model fitted to the consolidated agent-task binary matrix, from which per-agent ability θ and per-task difficulty β are estimated on","core_discovery":"Messier's central claim is that per-criterion, per-verifier records—not just task-level pass/fail—are the right unit for comparing AI agents across benchmarks. From those records, the authors demonstrate counterfactual rescoring: rescoring the same trials under soft (mean fraction of criteria passed) or majority-pass rules instead of the official all-pass rule raises measured success substantially and, on some benchmarks, changes agent orderings. They then fit a 1PL Rasch model to the consolidated agent-task response matrix and show that the resulting ability estimates align with the published ranking of an external capability index at Spearman ρ = 0.81 (95% CI [0.68, 0.89]) for general capa","pith_inferences":["Editorial extension: if per-verifier records become a release standard, audits for reward hacking, sandbagging, and evaluation awareness could run on archived trials at the exact granularity where those failure modes occur, without new compute.","Editorial extension: the observed rank reordering under different aggregation rules implies that any single-number leaderboard is partly a choice of scoring philosophy; competitive pressure on all-pass leaderboards may push agents to satisfy every criterion rather than to maximize partial progress, a behavior that may not match deployment value.","Editorial extension: the modest within-benchmark difficulty-prediction gains suggest task text encodes hardness beyond benchmark identity; a testable next step is using such estimates to build stratified test sets with calibrated difficulty for future benchmarks.","Editorial extension: a direct check of the verifier-transfer assumption—comparing the locally rebuilt rubric judge's per-criterion outcomes against the upstream benchmark's official scores on a shared subset—would quantify how much of the aggregation-effect magnitudes depend on the local judge."],"forward_implications":["If evaluations release criterion-level outcomes, any aggregation rule can be computed post hoc, making leaderboards re-auditable without rerunning agents.","Strict all-pass scoring should be read as a lower bound on progress: on multi-verifier tasks it can hide substantial partial completion, as the 0.2% vs 28.8% gap illustrates.","Open, harmonized records can reproduce an established capability ordering (ρ = 0.81) without a new evaluation campaign, lowering the cost of capability scaling.","Capability scales can be specialized by occupation, action space, or verifier type, enabling domain-specific rather than global comparisons.","Frontier progress is uneven: function calling is near saturation, programming shows the largest gains, and enterprise workflows remain the hardest frontier."],"fun_headline_variants":["Strict grading warps AI agent rankings, 957k-trial corpus finds","Soft rescoring uncovers AI progress hidden by all-pass tests","Per-criterion records expose agent capability shifts","Messier corpus links agent abilities to Epoch scales at rho 0.81"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the per-criterion verifier outcomes for the newly contributed benchmarks, especially the legal-workflow benchmark whose rubric was rebuilt locally as an LLM judge, faithfully reproduce the upstream benchmark's own grading; the paper reports no validation of this local verifier against official scores.","fun_headline_variants_meta":{"raw":{"variants":["Strict grading warps AI agent rankings, 957k-trial corpus finds","Soft rescoring uncovers AI progress hidden by all-pass tests","Per-criterion records expose agent capability shifts","Messier corpus links agent abilities to Epoch scales at rho 0.81"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000248,"raw_usage":{"total_tokens":1418,"prompt_tokens":812,"completion_tokens":606,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":556,"completion_tokens_details":{"reasoning_tokens":530}},"tokens_in":556,"tokens_out":606,"duration_ms":5274,"temperature":1.0,"reasoning_tokens":530,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T01:10:09.169380+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a multi-verifier benchmark that publicly releases both official task-level scores and individual criterion outcomes, and check whether the paper's rescoring pipeline exactly reproduces the official all-pass scores from the criterion outcomes; a mismatch would indicate the pipeline (or its local judge) diverges from the upstream grader. Concretely, for the six contributed benchmarks, run the upstream benchmarks' original verifiers on the same recorded traces and compare per-criterion outcomes with the local LLM judge's judgments—if they disagree on a large fraction of criteria, the headlin","supporting_citations":[],"review_version":1}