ARGOS:Automated Functional Safety Requirement Synthesis for EmbodiedAI via Attribute-Guided Combinatorial Reasoning

Ensuring functional safety is essential for deploying Embodied AI in complex open-world environments. Traditional Hazard Analysis and Risk Assessment (HARA) relies on enumerating risks for finite, pre-defined function lists, yet Embodied AI operates on open-ended natural language instructions whose combinatorial interactions give rise to unbounded hazard scenarios. Large Language Models (LLMs) offer a promising path to scalability, but without physical grounding they fall back on statistical correlations and overlook long-tail coupled risks. To address these limitations, we present ARGOS (Attribute-Guided Combinatorial Reasoning), a two-stage pipeline that bridges open-ended instructions and physical constraints. Stage I decomposes entities into fine-grained physical attributes and performs bounded combinatorial reasoning to discover multi-factor hazard scenarios. Stage II integrates these scenarios with robot capabilities and regulatory standards to synthesize auditable Functional Safety Requirements (FSRs). Experiments across two backbone models show that ARGOS significantly outperforms intrinsic-knowledge and chain-of-thought baselines in hazard scenario quality (Cohen's d=0.62–1.00). ARGOS also achieves the highest Overall score against full-rulebook long-context and STPA/FMEA-structured baselines (8.57 vs. 7.78 and 7.57). Human evaluation further reveals a Verbosity Trap: chain-of-thought reasoning generates redundant scenarios that inflate automated metrics yet are penalized by human experts. Stage-II ablations show that physical grounding consistently improves requirement synthesis and expose a Regulatory Mismatch on the primary backbone when regulatory knowledge is applied without sufficient physical context. Together, these findings establish explicit attribute grounding as a critical prerequisite for reliable safety reasoning in Embodied AI. The source code is available at https://anonymous.4open.science/r/ARGOS-274E.