Youmna Farag, Svetlana Stoyanchev, Simon Keizer, Mohan Li, Rama Doddipatla · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28381 · open-access PDF
大規模言語モデル(LLMs)で動作するエージェントにとって、安全性について考えることは不可欠です。Contextual Action and Plan Safety Benchmark(CAPSafe)を導入し、セキュリティ推論を2つのレベルで評価するように設計されています:(1)アクションレベルのデータセットで、単一のアクションがその環境状況に基づいて安全かどうかをテストし、(2)エージェントが複雑な危険な家事任務に安全な長期計画を生成できるかどうかを評価するプランレベルのデータセットです。
Machine translation (Meta M2M100); the English original follows.
Reasoning about safety is essential for embodied agents powered by large language models (LLMs). We introduce the Contextual Action and Plan Safety benchmark (CAPSafe), designed to evaluate safety reasoning at two levels: (1) an action-level dataset that tests whether a single action is safe given its environmental context, and (2) a plan-level dataset that evaluates whether agents can generate safe long-horizon plans for complex hazardous household tasks. CAPSafe models safety conditions where hazards are situational (inferred from object properties), multiple hazards can co-occur within the
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