Auditing Belief-Conditioned LLM Agents in Hidden-Information Social Deduction Games
Evaluating LLM agents in hidden-information multi-agent settings is hard: final outcomes are high-variance and rarely reveal why an agent decided as it did. We study this in a 9-player Werewolf environment where agents act under strict, code-level information isolation, and we build an auditable framework that maintains an external belief state over hidden roles, logs belief updates and belief-action deviations as structured evidence, and supports a defensive offline improvement loop that reviews bad cases before any strategy change. Across 1,080 frozen games spanning belief-disabled, active-belief, kernel-ablation, camp-restricted, consumption-policy, and high-load arms, and including a ...
| Authors | Yuan Gao, Jiangyi Yang, Yao Zhao et al. |
|---|---|
| Primary category | cs.MA |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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