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New CAPSafe benchmark reveals LLMs struggle with long-horizon safety planning

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CAPSafe, a new benchmark designed to evaluate the safety reasoning capabilities of embodied agents powered by large language models. The benchmark assesses safety at both the action and long-horizon planning levels, considering situational hazards and their severity. While several models demonstrated high accuracy in recognizing unsafe individual actions, robust safety reasoning over extended action sequences remains a significant challenge. Gemini-3-Pro performed best in safe long-horizon planning, achieving a 78.19% success rate, though other models lagged considerably. AI

IMPACT Highlights the need for improved long-horizon planning and safety reasoning in LLM-powered embodied agents.

RANK_REASON The cluster contains a new academic paper introducing a benchmark for evaluating AI safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CAPSafe benchmark reveals LLMs struggle with long-horizon safety planning

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The cluster contains a new academic paper introducing a benchmark for evaluating AI safety. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Youmna Farag, Svetlana Stoyanchev, Simon Keizer, Mohan Li, Rama Doddipatla ·

    CAPSafe: A Benchmark for Contextual Action and Plan Safety Reasoning in Embodied Agents

    arXiv:2610.28381v1 Announce Type: cross Abstract: 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 a…

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