MARBO: Relational Belief Grounding for LLM Agents in Social Deduction Games
Social deduction games (SDGs) require agents to reason under partial observability by maintaining relational beliefs about hidden roles and team alignments. While recent LLM-agent approaches improve gameplay through prompting and preference optimization, they often optimize actions and in-game speech without explicitly grounding them in such beliefs. This frequently leads to strategically inconsistent behavior, especially for compact LLM agents. We introduce Multi-Agent Relational Belief Optimization (MARBO), a belief-grounded preference optimization framework that leverages relational beliefs to guide strategic decisions and in-game speech. MARBO provides preference feedback only when be...
| Authors | Yechan Hwang, Sangjun Bae, Jeongmo Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 September 2026 |
| Last updated at source | 22 September 2026 |
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