Beyond the Transcript: Asymmetric Cognitive Graphs for LLM Agents in Social Deduction Games

Social deduction requires more than identifying hidden roles: a useful belief must guide actions that preserve credibility and advance a team's objective. We introduce Asymmetric Cognitive Graphs (ACG), an external belief-to-action framework for large language model agents inspired by these demands of human strategic play. ACG separates observed events, private role beliefs, and perceived public attitudes. Probabilistic evidence updates maintain graph memory, while role-conditioned planning balances progress, information, exposure, and coordination through explicit, game-specific action scores. Deployment on either or both sides tests how this integrated intervention redistributes team advantage. We evaluate four settings: a GRAIL-aligned disclosure comparison, role-rich six-player Avalon, homogeneous nine-player Werewolf, and a nine-model Werewolf roundtable. Across three Avalon models, both-side ACG increases Good-side win rates, with two contrasts surviving multiplicity correction; DeepSeek Flash rises from 6.7% to 43.3% over 30 paired seeds (exact McNemar $p=0.0034$). Against a GRAIL port, GPT-4.1 ACG achieves 65% versus 30% under member-attributed disclosure, but 20% versus 40% under binary outcomes; these comparisons remain descriptive. Across three homogeneous Werewolf models, Good-only deployment lowers recipient win-rate estimates, whereas Evil-only raises them. The heterogeneous roundtable yields repeatable behavioral profiles without significant paired win-rate differences. These results establish an inspectable architecture for translating social evidence into role-dependent action and reveal how the benefits of structured guidance vary across factions and interaction settings.