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arXiv cs.AI· Jiandong Ding, Honglei Ji, Ming Liu, Tao Duan·· 9 天前AI 评分26

Agent 技能检索中的同能力风险暴露:SameCapRisk-Bench 基准发布

Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval

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研究提出 SameCapRisk-Bench,包含 890 个单元和 1,314 个查询案例,覆盖五种机制与十二种冲突类型,用于评估技能检索中"主题匹配但资源、流程或输出要求冲突"的同能力风险暴露。

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Abstract:A skill can match a task's topic while conflicting with its resource, procedure, or output requirements. We study this as same-capability risk-exposure retrieval and introduce SameCapRisk-Bench: 890 units and 1,314 query cases across five mechanisms and twelve conflict types. Each unit pairs a skill that meets a query requirement with a same-capability skill that violates it, with evidence for that distinction. The evaluation tests source-task requirements and two kinds of paired queries that reverse which skill fits: changing the requested evidence role or exact output interface while holding the skills fixed. To capture both retrieval success and conflicting exposure, Recall tracks helpful hits, harmful sibling rate (HSR) tracks exposure of the conflicting sibling, and CleanHit requires a helpful hit without that exposure. Four public skill retrievers expose conflicting siblings at HSR@3 of 0.737-0.881 on source-task contracts, compared with 0.099-0.12 on controlled source-role queries. Across the fixed mixture of 1,235 held-out queries, their Recall@3 is 0.903-0.944 and HSR@3 is 0.344-0.393. The conflicting sibling ranks first on 24.8-29.2% of source-task queries for these four retrievers. We also examine what the reranker receives: truncating long skills can remove the passages where the two skills' contracts differ. With the same BGE top-20 candidates, increasing the reranker's input budget from 512 to 4,096 tokens reduces source-task sibling-first errors by 8.27 pp, with an uncertain CleanHit gain.
Comments: Preprint. Supersedes arXiv:2606.10388
Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.10388 [cs.IR]
  (or arXiv:2606.10388v4 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2606.10388

arXiv-issued DOI via DataCite

Submission history

From: Jiandong Ding [view email]
[v1] Tue, 9 Jun 2026 03:54:45 UTC (880 KB)
[v2] Thu, 20 Aug 2026 01:59:33 UTC (850 KB)
[v3] Fri, 11 Sep 2026 09:19:38 UTC (2,380 KB)
[v4] Thu, 8 Oct 2026 08:56:27 UTC (2,242 KB)

来源:arXiv cs.AI · arxiv.org