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        <namePart type="given">JunJian</namePart>
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    <abstract>Large Language Models (LLMs) exhibit impressive reasoning capabilities but often suffer from Embodied Semantic Hallucinations—generating plans that are semantically fluent but physically unsafe due to a lack of grounded common sense. Existing safety alignment methods, such as RLHF or naive safety prompting, typically fall into a Safety-Utility Trade-off, resulting in severe over-rejection of benign household instructions. To address this, we propose MADRA (Multi-Agent Debate for Risk Awareness), a training-free cognitive architecture that mimics System-2 deliberation. MADRA introduces a meta-cognitive Critical Agent that evaluates peer debates using a structured argumentation framework derived from the Toulmin Model, effectively mitigating the “herd mentality” in multi-agent systems. We also introduce SafeAware-VH, a benchmark featuring adversarial safe instructions designed to probe agents’ sensitivity to physical risks. Extensive experiments demonstrate that MADRA breaks the Pareto frontier, achieving over 90% rejection of unsafe tasks while maintaining high utility, significantly outperforming standard Chain-of-Thought and single-agent reflection baselines.</abstract>
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%0 Conference Proceedings
%T MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning
%A Wang, JunJian
%A Zhao, Lidan
%A Zhang, Xi Sheryl
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-madra
%X Large Language Models (LLMs) exhibit impressive reasoning capabilities but often suffer from Embodied Semantic Hallucinations—generating plans that are semantically fluent but physically unsafe due to a lack of grounded common sense. Existing safety alignment methods, such as RLHF or naive safety prompting, typically fall into a Safety-Utility Trade-off, resulting in severe over-rejection of benign household instructions. To address this, we propose MADRA (Multi-Agent Debate for Risk Awareness), a training-free cognitive architecture that mimics System-2 deliberation. MADRA introduces a meta-cognitive Critical Agent that evaluates peer debates using a structured argumentation framework derived from the Toulmin Model, effectively mitigating the “herd mentality” in multi-agent systems. We also introduce SafeAware-VH, a benchmark featuring adversarial safe instructions designed to probe agents’ sensitivity to physical risks. Extensive experiments demonstrate that MADRA breaks the Pareto frontier, achieving over 90% rejection of unsafe tasks while maintaining high utility, significantly outperforming standard Chain-of-Thought and single-agent reflection baselines.
%U https://aclanthology.org/2026.findings-acl.340/
%P 6852-6876

Markdown (Informal)

[MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning](https://aclanthology.org/2026.findings-acl.340/) (Wang et al., Findings 2026)

• MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning (Wang et al., Findings 2026)

ACL

• JunJian Wang, Lidan Zhao, and Xi Sheryl Zhang. 2026. MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6852–6876, San Diego, California, United States. Association for Computational Linguistics.