{"id":"cd35e997-99e7-42c9-8080-dab14e714791","arxiv_id":"2606.10388","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"SkillResolve-Bench supplies 661 helpful/risky pairs and SkillResolve achieves Recall@3 0.766, NDCG@3 0.699 and HSR@3=0 by resolving within-family ambiguity, outperforming SkillRouter while eliminating harmful sibling exposure at K=3.","lead":"The paper creates SkillResolve-Bench, a dataset of 661 helpful/risky skill pairs that share capability families but differ in execution risks such as stale resources or missing preconditions, plus the SkillResolve method that groups candidates into families and picks one representative per family. Smart generalists might read it because agent systems increasingly rely on skill libraries and this work targets a concrete failure mode that can cause execution errors.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Benchmark construction of the 661 pairs and family relations may embed artificial same-capability distinctions","rationale":"The reader's weakest_assumption is precisely the load-bearing condition; the full-text availability does not remove the need for external validation of construction fidelity, so the verdict remains conditional on that check rather than UNVERDICTED or ACCEPT.","tokens_in":1894,"tokens_out":352,"duration_ms":15311,"concrete_test":"Extract the precise construction procedure for the 661 pairs and family relations from the full manuscript (benchmark section); independently sample 50 pairs and have two external domain experts rate each on a 1-5 realism scale for actual agent execution risk; if mean realism <3.5 or inter-rater kappa <0.6, recompute all reported metrics on the high-realism subset only.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The headline metrics (Recall@3 0.766, NDCG@3 0.699, HSR@3=0) and the ablation isolating representative selection as the mechanism both presuppose that the released family relations and helpful/risky pairs reflect genuine execution-risk distinctions that arise in real agent skill libraries. If the pairs were generated via manual curation, synthetic risk injection, or non-representative sampling rather than direct extraction from public libraries with minimal intervention, then the zero HSR and the reported gains over SkillRouter become benchmark artifacts; the claim that within-family selection converts capability retrieval into safer procedural exposure would not transfer. The abstract lists source-role evidence, cue/leakage checks, and query-disjoint splits but supplies no quantitative audit of construction bias or inter-annotator agreement on risk realism.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that skill retrieval for agent libraries can surface the wrong same-capability representative, exposing execution risks even when the capability family is correct; it introduces SkillResolve-Bench (661 helpful/risky pairs, 7,982-candidate pool, query-disjoint splits, cue/leakage checks) and the SkillResolve method (family resolution + query-conditioned scoring + representative selection) that reports Recall@3=0.766, NDCG@3=0.699, HSR@3=0, outperforming SkillRouter by 0.112/0.165 while driving HSR@3 from 0.693 to 0, with an ablation showing HSR@3 rises to 0.236 without representative selection.","tokens_in":2068,"tokens_out":405,"duration_ms":17120,"significance":"If the benchmark pairs and family relations validly reflect real execution-risk distinctions, the work identifies a practically important failure mode in agent skill retrieval and supplies both a measurable benchmark and a concrete mitigation (within-family representative selection) whose contribution is isolated by ablation; the released benchmark and explicit ablation isolating the representative-selection step are strengths that support reproducibility and mechanistic insight.","major_comments":[{"comment":"Abstract (benchmark construction paragraph): the headline metrics (Recall@3 0.766, NDCG@3 0.699, HSR@3=0) and the claim that representative selection turns capability retrieval into safer procedural exposure presuppose that the 661 helpful/risky pairs and released family relations capture genuine same-capability execution-risk distinctions arising in real libraries; the text lists source-role evidence, cue/leakage checks, and query-disjoint splits but supplies no inter-annotator agreement figures, quantitative bias audit, or sampling audit, leaving the zero-HSR result and the ablation unsupported for transfer.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive review and for acknowledging the practical importance of same-capability ambiguity as well as the strengths of the released benchmark and ablation. We address the single major comment below.","responses":[{"response":"We agree that the manuscript would benefit from expanded quantitative validation of the benchmark construction. The 661 pairs were produced via deterministic source-role extraction from the original SkillRet library metadata together with the 6,660 public candidates; risk labels follow three explicitly defined execution-risk categories (stale resource, missing precondition, wrong procedure). Cue/leakage checks and query-disjoint splits were applied to eliminate trivial or contaminated queries. Because the labeling process was rule-based rather than free-form subjective annotation, inter-annotator agreement was not computed. In the revised version we will add a dedicated “Benchmark Construction Validation” subsection that reports: (i) a sampling audit giving the distribution of risk types and family sizes, (ii) a quantitative bias audit comparing family statistics in the 661-pair set against the full 7,982-candidate pool, and (iii) explicit discussion of how the source-role evidence supports transfer to other libraries. These additions will strengthen the support for the reported HSR@3=0 result and the ablation isolating representative selection. The ablation itself remains internally valid because it holds the scorer fixed and varies only the representative-selection step.","revision_made":"yes","referee_comment":"[Abstract] Abstract (benchmark construction paragraph): the headline metrics (Recall@3 0.766, NDCG@3 0.699, HSR@3=0) and the claim that representative selection turns capability retrieval into safer procedural exposure presuppose that the 661 helpful/risky pairs and released family relations capture genuine same-capability execution-risk distinctions arising in real libraries; the text lists source-role evidence, cue/leakage checks, and query-disjoint splits but supplies no inter-annotator agreement figures, quantitative bias audit, or sampling audit, leaving the zero-HSR result and the ablation unsupported for transfer."}],"tokens_in":1535,"tokens_out":439,"duration_ms":12967,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing here is a benchmark that isolates same-capability execution-risk retrieval, where a retriever can surface the right skill family but the wrong representative that carries execution hazards like stale resources or bad procedures. SkillResolve adds family resolution plus representative selection and reports Recall@3 at 0.766, NDCG@3 at 0.699, and HSR@3 at 0, beating SkillRouter while the ablation shows the selection step is what drives HSR down from 0.236.\n\nThe setup is concrete: 661 pairs, a 7,982-candidate pool pulled from public sources, query-disjoint splits, and cue/leakage checks. The ablation cleanly identifies the mechanism, which is the strongest part of the evidence.\n\nThe soft spot is benchmark construction. The abstract lists source-role evidence and checks but supplies no numbers on how the helpful/risky pairs were actually made or any agreement measure. If the distinctions were injected or curated in ways that do not match real agent libraries, the zero HSR result and the transfer claim stay tied to this particular data. That is the main open question.\n\nThis is for researchers working on skill libraries and retrieval for agents who need to think about downstream execution safety, not just relevance. Readers in that corner will find the benchmark and the family-resolution step worth testing.\n\nThe work shows straightforward engagement with the retrieval setting and the claims are falsifiable on the released data. It deserves peer review because the problem is practical and the empirical pieces are specific enough to evaluate and extend.","headline":"SkillResolve-Bench and the family-resolution method target same-capability ambiguity in agent skill retrieval with measurable gains on harmful sibling rate.","tokens_in":2511,"tokens_out":388,"would_cite":false,"duration_ms":20487,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Agent skill retrieval can eliminate exposure to risky same-capability variants by selecting family representatives.","keywords":["skill retrieval","agent skills","same-capability ambiguity","harmful sibling rate","representative selection","benchmark","information retrieval","execution risk"],"falsifier":"Running the benchmark on an independent collection of agent skills and checking if the HSR@3 remains zero for the SkillResolve method while maintaining the reported recall levels.","tokens_in":2795,"feed_emoji":"","tokens_out":696,"duration_ms":25672,"temperature":0.7,"pith_summary":"The paper shows that retrieving the right capability family is not enough for agent skill libraries because the wrong representative can introduce execution risks such as stale resources or incorrect procedures. SkillResolve-Bench provides 661 helpful/risky pairs along with family relations to evaluate both ranking quality and the rate of harmful sibling exposure. The SkillResolve method identifies families, scores utility using confusable negatives and cues, then picks one representative per family. This yields Recall@3 of 0.766 and NDCG@3 of 0.699 with zero harmful sibling rate at K=3, outperforming prior methods.","feed_headline":"Skill retrieval hits 0.766 recall with zero harmful siblings","feed_subtitle":"Family representative selection avoids risky same-capability variants and beats baselines on a new benchmark of 661 pairs.","key_machinery":"Within-family representative selection after resolving capability families, which prevents harmful sibling exposure in top-K results.","core_discovery":"Each query in the benchmark pairs a helpful skill with a query-specific risky sibling that shares the capability family but can lead to execution problems. SkillResolve resolves active candidate families, scores query-conditioned utility from confusable library negatives and contract-profile cues, and selects one representative from each family before the final top-K list. Under the released family relation, SkillResolve reaches Recall@3 0.766 and NDCG@3 0.699 while keeping HSR@3=0. It improves over SkillRouter by 0.112 Recall@3 and 0.165 NDCG@3 while reducing HSR@3 from 0.693 to 0. Without representative selection, HSR@3 rises to 0.236 under the same scorer.","pith_inferences":["Libraries could adopt family annotations to support safer skill composition in agents.","The approach may extend to other domains like code retrieval where similar implementations carry different risks.","Real-world agent runs on benchmark tasks could test if lower HSR correlates with fewer failures."],"forward_implications":["The representative selection mechanism reduces HSR@3 to 0 while preserving high Recall@3 and NDCG@3.","Omitting representative selection increases HSR@3 to 0.236 with the same scoring function.","SkillResolve improves Recall@3 by 0.112 and NDCG@3 by 0.165 compared to SkillRouter.","The benchmark supports auditing through source-role evidence, cue/leakage checks, and query-disjoint splits."],"fun_headline_variants":["SkillResolve selects reps for 0.766 recall@3 and zero HSR@3","Family rep selection yields 0.766 recall@3 with HSR@3 at zero","Same-capability resolution reaches 0.766 recall@3 zero HSR@3","SkillResolve-Bench reports 0.766 recall@3 at HSR@3 of zero"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The 661 helpful/risky pairs and the family relations supplied with the benchmark correctly capture genuine same-capability execution-risk distinctions that occur in real agent skill libraries, without artificial construction artifacts or unrepresentative sampling.","fun_headline_variants_meta":{"raw":{"variants":["SkillResolve selects reps for 0.766 recall@3 and zero HSR@3","Family rep selection yields 0.766 recall@3 with HSR@3 at zero","Same-capability resolution reaches 0.766 recall@3 zero HSR@3","SkillResolve-Bench reports 0.766 recall@3 at HSR@3 of zero"]},"model":"grok-4.3","cost_usd":0.005125,"raw_usage":{"total_tokens":2582,"prompt_tokens":849,"num_sources_used":0,"completion_tokens":93,"cost_in_usd_ticks":51249500,"prompt_tokens_details":{"text_tokens":849,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1640,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":849,"tokens_out":93,"duration_ms":12659,"temperature":1.0,"reasoning_tokens":1640,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T11:51:58.007575+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the benchmark on an independent collection of agent skills and checking if the HSR@3 remains zero for the SkillResolve method while maintaining the reported recall levels.","supporting_citations":[],"review_version":1}