{"id":"139959ef-f5ad-4133-86c8-d179c892a48c","arxiv_id":"2608.05573","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"SkillTV-Bench provides a multi-domain, skill-aware trajectory verification benchmark, and SkillTV-Evolve's evolved JudgeSkill improves an agent judge's accuracy by 14.8 points on a held-out set.","lead":"This paper introduces SkillTV-Bench, a 681-case benchmark of real agent trajectories with task-time skills and inspectable artifacts, and SkillTV-Evolve, a method that iteratively improves a textual judge skill from misjudged cases. The refined skill raises the same agent judge's accuracy by 14.8 percentage points and improves selected-trajectory success from 22.9% to 45.5% at ten rollouts.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Ground-truth labels are taken verbatim from SkillsBench's source verifier with no human validation; if those labels are noisy, the 14.8-pp improvement may reflect overfitting to the verifier's biases rather than true task success.","rationale":"The paper has genuinely good internal validity: a task-disjoint split, a fixed 36-case development gate, paired McNemar testing, and monotone v1-v4 progress, so the +14.8 pp gain is not obviously a leakage artifact. However, those controls all operate relative to the same unvalidated labels. The strongest claim is about verification of skill-augmented agent executions in the world, not about matching SkillsBench's source verifier. If label error is nontrivial, both the diagnosis (judges accept plausible failures) and the remedy (JudgeSkill reduces false accepts from 105 to 71) could be partially illusory. The concrete test I propose, human relabeling of a stratified sample, directly targets this premise. Note the small evaluation set (203 cases, with domain bins of 13-30) means the aggregate p-value is driven by roughly 30 flips; label noise on even 10-15 of those cases would change the conclusion. I did not find a separate internal-inconsistency issue, so I do not propose changing the reader's conditional verdict.","tokens_in":12834,"tokens_out":4765,"duration_ms":66438,"concrete_test":"Take a stratified sample of at least 100 cases from the 203-case evaluation set (covering all nine domains; if feasible, all 681 cases). Hide the source-verifier labels and have two or more independent annotators with domain expertise judge pass/fail from the full instruction, trajectory, skill set, and artifacts, after agreeing on a written rubric; measure inter-annotator agreement. Recompute Table 2 rows for the baseline agent judge and the refined JudgeSkill, and Table 5 success@10, using the human-validated labels. If agreement with the source verifier is below about 95%, or if the accuracy gain shrinks by more than a few points or loses significance, the headline claim must be qualified as an improvement relative to the source verifier rather than to true task success.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that the refined JudgeSkill improves trajectory-verification accuracy by 14.8 percentage points (Table 2) and selected-trajectory success from 22.9% to 45.5% (Table 5). Every one of those numbers is computed against labels y_i inherited from SkillsBench's source verifier (Eq. 1; Benchmark construction section). The construction pipeline filters 1,274 trials to 681, retains source tasks with both pass and fail executions, and validates schema conformance, but it never independently checks whether the source verifier's pass/fail decision matches actual task success. This is the load-bearing premise because the benchmark's purpose is to expose plausible-but-failed executions; if the source verifier has systematic blind spots, the baseline judges' low precision and the refined JudgeSkill's precision gain could both reflect learning the verifier's biases rather than true verification. The risk is amplified by the evolution loop, which optimizes the JudgeSkill against these same labels (Eq. 4). There is no internal inconsistency here, but external validity of the main claim depends on label accuracy, and the paper provides no label-error analysis.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"SkillTV-Bench is a 681-case trajectory-verification benchmark derived from the authors' prior SkillsBench executions, spanning 50 source tasks and eleven domains, and split into a 478-case evolution pool and a task-disjoint 203-case evaluation set. Each judge case packages the original instruction, a normalized step-wise trajectory, the task-time skills, and inspectable artifacts, with pass/fail labels inherited from SkillsBench's automated source verifier. The paper reports that existing judges (GPT-5.2, Claude Sonnet 4.6, Gemini 3.1 Pro, majority vote, rubric-based judging, and a tool-using agent judge) all perform poorly on the benchmark, primarily by accepting plausible-but-failed executions. The proposed SkillTV-Evolve method externalizes verification knowledge as a human-readable JudgeSkill that structures judging into an inspection plan, an inspection log, and an evidence-grounded verdict, and iteratively rewrites the skill with an LLM-based optimizer whose candidates are promoted only if they improve a fixed 36-case development gate. The refined skill v4 improves the same Claude-Sonnet-4.6-based agent judge from 43.8% to 58.6% accuracy (McNemar p < 10^-4) and from 0.568 to 0.634 balanced accuracy on the held-out set, and increases verifier-guided selected-trajectory success at ten rollouts from a 22.9% single-rollout base rate to 45.5%, with all intermediate skill versions reported.","tokens_in":13065,"tokens_out":17811,"duration_ms":142261,"significance":"If the headline numbers survive an independent label check, this is a solid, well-scoped contribution. The benchmark fills a genuine gap - skill-aware, evidence-grounded, agentic trajectory verification - and the +14.8 pp gain from an explicit, externally evolvable skill, with the underlying model held fixed, is a concrete and falsifiable result. I specifically credit the task-disjoint held-out split, the fixed development gate that prevents evaluation-label feedback, the paired significance test, the monotone held-out improvement across four skill versions, the transparent per-domain table that shows two zero-gain domains, the honest limitation paragraph that confines the claims to offline verification and selection, and the release of code, data, and named harness versions (Claude Code v2.1.19, Harbor). The main risk is external validity: every number in Tables 2-5 is measured against source-verifier labels whose fidelity to true task success is never tested, so the magnitude of the reported improvements should be read as conditional on label quality.","major_comments":[{"comment":"Eq. (1) and the 'Benchmark construction' section define y_i as the source-verifier label inherited from SkillsBench, and the five-stage construction pipeline validates schema conformance, token limits, and cross-field consistency but never the correctness of the pass/fail decision itself. This is the load-bearing premise for the headline numbers: all of Table 2, Table 5, and the evolution objective in Eq. (4) are computed against these labels, and the benchmark's stated purpose is precisely to expose 'plausible-but-failed' executions, which is the class most vulnerable to verifier blind spots. If the source verifier mislabels a substantial fraction of the 681 cases, then the measured judge weaknesses and the +14.8 pp gain could partly reflect the skill learning the verifier's biases on 36 task families of the same SkillsBench lineage, rather than learning to detect true task failure. I do not regard this as a circularity problem, because the evaluation set is task-disjoint and the evaluation labels never enter evolution; it is an untested external-validity assumption. Please add a human-annotation audit on a stratified sample of the 681 labels (I would suggest 100-200 cases judged with the source-verifier output blinded), report agreement and disagreement patterns, and recompute the headline results on the human-confirmed subset, or alternatively state the inherited-label assumption explicitly and bound how label error would move the 14.8 pp estimate.","section":"Benchmark construction; Eq. (1)"},{"comment":"Two properties of the development gate in SkillTV-Evolve need clarification and strengthening. First, the sentence describing the gate as 'a fixed 36-case development gate that preserves all 14 task families' is ambiguous in a way that touches the paper's non-circularity guarantee: the evaluation set also contains 14 source tasks, and if any of the gate's 36 cases come from those tasks, then skill promotion uses evaluation-task labels and the claimed disjointness is compromised. Please state explicitly that the gate is drawn exclusively from the 478-case evolution pool and is task-disjoint from the 203-case evaluation set, and define the phrase 'matches the full-set baseline behavior,' which is currently undefined. Second, the promotions between adjacent versions in Table 4 rest on gate-score differences of at most 1.1 points (G = 49.2, 50.3, 52.6, 52.7) on a 36-case gate, and the v3-to-v4 promotion in particular is a 0.1-point difference that is well within the sampling noise of such a small set. The monotone held-out improvement from 50.8% to 58.6% across versions is reassuring evidence that the progression is not pure selection noise, but the paper should report bootstrap confidence intervals for G at each version, or enlarge the gate, before claiming that the loop reliably promotes better skills.","section":"SkillTV-Evolve development gate; Eq. (4); Table 4"},{"comment":"The evolution loop is not reproducible from the paper as written. The 'Automated JudgeSkill evolution' section does not specify the optimizer (it says only that 'an optimizer receives the current skill... and proposes a revised skill'), the model and configuration behind it, the number of evolution rounds, the sampling scheme for the 'sampled development executions,' or the per-round budget. Most importantly, the full texts of JudgeSkill v1-v4, and especially the final v4 skill that produces the headline numbers, are not included in the paper or appendix, even though the method's contribution is exactly an explicit, auditable, human-readable verification skill. The same gap applies to the three judge-facing failure-prior references synthesized from prior failure analyses, whose content is never described. Without the skill text and the optimizer configuration, readers cannot reproduce SkillTV-Evolve, cannot audit what was learned, and cannot test the claim that verification knowledge has been externalized; please include the full skill texts and complete loop hyperparameters, or point precisely to where they are released in the repository.","section":"Automated JudgeSkill evolution section; RQ2"}],"minor_comments":[{"comment":"In the RQ3 protocol, the definition of the 'Best rollout' column in Table 5 is not given, and the selection rule for the case where no trajectory is predicted as pass is not stated; the paper should also confirm that every one of the 14 source tasks has at least 10 stored trajectories, since 'sampling n without replacement' for n = 10 requires pool sizes of at least 10.","section":"RQ3 protocol; Table 5"},{"comment":"For the main paired comparison (43.8% vs 58.6%, Table 2), please report the McNemar discordant-pair counts (b and c) and a confidence interval for the 14.8 pp difference, rather than the p-value alone.","section":"RQ2; Table 2"},{"comment":"Because 156 of 203 evaluation cases are failures (76.8%), a trivial all-fail classifier would achieve 76.8% accuracy, above every method in Table 2; adding this majority-class baseline to the Table 2 discussion and noting that the balanced-accuracy and rollout-selection results are the more robust evidence would help readers interpret the accuracy numbers.","section":"Table 2 and RQ1"},{"comment":"In 'Benchmark construction,' the sentence 'We believe there is no need to introduce verifiers to select pass candidate if the agentic trajectories of the task are completely correct or wrong' is ungrammatical, and the underlying design choice (dropping all-pass and all-fail tasks) deliberately inflates benchmark difficulty; please state the rationale explicitly and note that the 22.9% single-rollout success rate is a construction-dependent quantity rather than a natural base rate.","section":"Benchmark construction"},{"comment":"In 'Automated JudgeSkill evolution,' the relationship between the gate's 'all 14 task families' and the evaluation set's '14 source tasks' should be clarified in one unambiguous sentence (this overlaps with major comment 2); likewise, 'prior empirical analyses paper' and a few other phrases in the same section need copyediting.","section":"Automated JudgeSkill evolution"}],"recommendation":"major_revision","confidential_remarks":"The label-validity concern in major comment 1 is the point I would prioritize: because the benchmark and the evolution-target labels both come from the authors' own prior SkillsBench pipeline, a human label audit, ideally performed by annotators not involved in the original SkillsBench runs, would substantially de-risk the external-validity claim. The manuscript fits the scope of an AI venue; the contribution is useful conditional on label quality, and the self-citation of SkillsBench is directly relevant rather than gratuitous. If the authors add the label audit and the missing method details, I would expect the paper to be very quickly acceptable."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Here's my take on SkillTV-Bench. The paper does two things: it ships a new benchmark for skill-aware trajectory verification with agentic inspection, and it shows that a text-based JudgeSkill, evolved on a disjoint development pool, improves the same agent judge's accuracy by 14.8 points on the held-out set. The experimental design is careful: task-disjoint split, fixed 36-case gate, McNemar test, per-domain breakdown. That's real work, and the benchmark fills a gap that Table 1 supports—nothing else combines skill awareness with agentic interaction in multi-domain trajectories. The rollout-selection result (45.5% vs 22.9% at ten rollouts) is a nice downstream demonstration that better verification translates into better selection.\n\nThe soft spot is the ground truth. All 681 labels are taken verbatim from SkillsBench's source verifier, with no human validation and no label-error analysis. Every accuracy number in the paper is computed against those labels. If the source verifier is systematically wrong on some classes of failures—plausible-but-failed executions are exactly the kind of thing automated verifiers miss—then the measured +14.8 pp improvement may partly reflect the JudgeSkill learning the verifier's biases rather than true task success. That doesn't make the study circular: the held-out set is disjoint, and the evolution loop uses only development labels. But it does mean the headline numbers should be read as 'agreement with the source verifier,' not 'agreement with ground truth.'\n\nTwo other issues are more minor. The eval set is 203 cases across 14 tasks, so per-domain numbers (Table 3) have wide variance; two domains show zero improvement. And the final JudgeSkill text is not in the paper, only a repo link without a commit hash, which hurts exact reproducibility for a method whose entire output is a text skill.\n\nOverall, this is a well-executed paper with a genuine contribution. The label question is the kind of thing peer review should push on—ask for a label-error analysis or a human-validated subset. If the labels hold up, this becomes a useful resource. If not, the benchmark still has value as a comparative tool, but the absolute claims need to be reworded. I'd send it to review with a request for label validation and artifact release.","headline":"Solid benchmark and careful study, but the inherited source-verifier labels are the load-bearing assumption; the +14.8 pp is improvement against that verifier, not absolute ground truth.","tokens_in":13640,"tokens_out":2320,"would_cite":true,"duration_ms":24576,"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":"A reusable text skill, evolved on disjoint examples, lifts an agent judge's trajectory-verification accuracy by 14.8 points.","keywords":["LLM-as-a-Judge","agent trajectory verification","skill-augmented agents","JudgeSkill","benchmark construction","rollout selection","test-time scaling","verifier evolution"],"falsifier":"Take a random sample of the 681 SkillTV-Bench cases and have independent human annotators (or a second verifier) relabel pass/fail from the packaged instruction, trajectory, skills, and artifacts; if a substantial fraction of labels flip, recompute the 14.8-point accuracy gain and the 45.5% success@10 against the corrected labels.","tokens_in":1711,"feed_emoji":"⚖️","tokens_out":4385,"duration_ms":91017,"temperature":0.7,"pith_summary":"SkillTV-Bench is a 681-case benchmark of real skill-augmented agent executions across 50 tasks and eleven domains, each packaged as instruction, normalized trajectory, task-time skills, and inspectable artifacts with a hidden source label. The paper claims that existing judges—direct LLMs, ensembles, rubric-based judges, and agent judges without a skill—systematically fail on this benchmark, mostly by accepting plausible-looking failed executions. It proposes SkillTV-Evolve, which externalizes verification knowledge as a reusable JudgeSkill that plans inspections, logs evidence, and issues verdicts, and refines that skill in a gated loop on a disjoint development pool. The central result is that the refined skill lifts the same agent judge's accuracy from 43.8% to 58.6% on the held-out set, and lifts selected-trajectory success at ten rollouts from 22.9% to 45.5%. If correct, this shows verification of long-horizon agent executions can be substantially improved without updating the model.","feed_headline":"Reusable judge skill lifts agent verdict accuracy 14.8 points","feed_subtitle":"Evolved on disjoint examples, the skill raises selected-trajectory success from 22.9% to 45.5% at ten rollouts.","key_machinery":"The central objects are the judge-case tuple (I, τ, S, A)—instruction, normalized trajectory, task-time skills, and artifacts—and the JudgeSkill, a human-readable procedure that decomposes judging into an inspection plan, an executed inspection log, and a final evidence-grounded verdict. The JudgeSkill carries the argument because it converts abstract rubric criteria into concrete evidence-seeking actions across artifact checks, skill-usage checks, and reasoning-process checks. The second mechanism is the gated evolution loop: misjudged development cases are compressed into rewrite briefs, an optimizer proposes revised skill text, and a fixed 36-case gate with weighted score G = 0.5*BalAcc + 0.3*Recall_fail + 0.2*Precision_pass decides promotion, so the skill improves without changing model weights.","core_discovery":"The core discovery is that verification of skill-augmented agent executions is a joint consistency problem over instruction, trajectory, skill use, and artifact, and that the dominant failure of existing judges is false acceptance of failed trajectories that look plausible. SkillTV-Bench operationalizes this by packaging each case as (I, τ, S, A) with a hidden source-verifier label, and the paper shows that frontier direct judges, ensembles, rubrics, and agent judges without a skill all land near or below 0.586 balanced accuracy, with high recall but low precision on the pass class. The paper's own mechanism, an explicit JudgeSkill that guides an agent judge through an inspection plan, an inspection log, and an evidence-grounded verdict, and that is refined on misjudged development cases through a gated evolution loop, reaches 58.6% accuracy and 0.634 balanced accuracy, and reduces false accepts from 105 to 71 on the evaluation set. The paper therefore claims that benchmark-grounded evolution can convert verification errors into reusable procedural knowledge, and that this knowledge transfers to offline rollout-pool selection, where the refined skill lifts selected-trajectory success to 45.5% at ten rollouts.","pith_inferences":["If the source-verifier labels contain noise, the +14.8-percentage-point gain could partly reflect label-noise patterns rather than true task success; a human relabeling study on a sample would clarify this.","The JudgeSkill may generalize to other backbone models or other trajectory sources, since the paper does not test this and the mechanism suggests domain-agnostic inspection heuristics.","The same evolution loop could be driven by other error signals—human preference judgments or downstream task outcomes—turning the verifier into a general learning signal rather than only a filter.","A closed loop that returns evidence-grounded feedback to task agents could make SkillTV-Bench self-expanding, because newly validated trajectories would refresh the development pool."],"forward_implications":["Judging long-horizon agent runs can be treated as a trainable artifact: a textual skill, not model weights, carries the improvement.","SkillTV-Bench's error profile implies that future judges should be evaluated for false-accept rate, not just overall accuracy, since plausible failures dominate.","Verification gains translate directly to test-time scaling: a cleaner predicted-pass set makes rollout selection more reliable, so investing in verifiers can be an alternative to adding more rollouts.","The evolution protocol is reusable: any judge that emits plans, logs, and verdicts can be improved on a disjoint development pool without touching the underlying LLM.","The paper's stated limitation means the method today filters candidates rather than improving task execution; returning judge feedback to task agents is the natural next step."],"supporting_citations":[{"why":"Supplies the 1,274 raw SkillsBench trials, task-time skills, and source-verifier pass/fail labels from which SkillTV-Bench is built.","marker":"Li et al. 2026a"},{"why":"JudgeBench, the representative LLM-judge benchmark that SkillTV-Bench positions against for final-response judging.","marker":"Tan et al. 2025"},{"why":"AgentRewardBench, the prior trajectory-level judge benchmark whose web-agent setting motivates SkillTV-Bench's design.","marker":"Lù et al. 2025"},{"why":"AJ-Bench, the agent-as-a-judge benchmark that SkillTV-Bench extends by adding task-time skills and inspectable environments.","marker":"Shi et al. 2026"},{"why":"Large Language Monkeys, the repeated-sampling result that frames rollout selection as the test-time-scaling setting SkillTV-Evolve improves.","marker":"Brown et al. 2024"},{"why":"Optimal test-time compute scaling, the theoretical backdrop for the claim that verifier quality determines the value of added rollouts.","marker":"Snell et al. 2024"},{"why":"AgentRx, one of the failure-analysis sources that inform the judge-facing failure priors synthesized into the JudgeSkill.","marker":"Barke et al. 2026"}],"fun_headline_variants":["SkillTV-Bench: skill lifts judge accuracy 14.8 points","Reusable judge skill: +14.8% accuracy, +22.6% rollout success","Judge skill evolution: accuracy up 14.8, rollout success up 22.6","Skill-augmented judge outperforms by 14.8 points"],"cache_read_input_tokens":15744,"weakest_assumption_plain":"The load-bearing premise is that the source verifier labels from SkillsBench are correct for all 681 cases; the paper adopts them without human validation or label-error analysis, so every accuracy and selection number stands or falls with that verifier.","fun_headline_variants_meta":{"raw":{"variants":["SkillTV-Bench: skill lifts judge accuracy 14.8 points","Reusable judge skill: +14.8% accuracy, +22.6% rollout success","Judge skill evolution: accuracy up 14.8, rollout success up 22.6","Skill-augmented judge outperforms by 14.8 points"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000442,"raw_usage":{"total_tokens":2291,"prompt_tokens":1052,"completion_tokens":1239,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":668,"completion_tokens_details":{"reasoning_tokens":1152}},"tokens_in":668,"tokens_out":1239,"duration_ms":18966,"temperature":1.0,"reasoning_tokens":1152,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-08T10:14:34.386435+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a random sample of the 681 SkillTV-Bench cases and have independent human annotators (or a second verifier) relabel pass/fail from the packaged instruction, trajectory, skills, and artifacts; if a substantial fraction of labels flip, recompute the 14.8-point accuracy gain and the 45.5% success@10 against the corrected labels.","supporting_citations":[],"review_version":1}