Bayesian Social Deduction with Graph-Informed Language Models
This work addresses the problem of enabling efficient and effective social reasoning in AI agents for interactive applications like games, though it is incremental in improving existing methods.
The paper tackled the challenge of social reasoning in large language models (LLMs) by developing a hybrid framework that combines structured probabilistic models with LLMs for language tasks, achieving a 67% win rate against human players in the Avalon game and outperforming baselines.
Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large language models (LLMs). We evaluate the limits of current reasoning language models in the social deduction game Avalon and find that while the largest models demonstrate strong performance, they require extensive test-time inference and degrade sharply when distilled to smaller, real-time-capable variants. To address this, we introduce a hybrid reasoning framework that externalizes belief inference to a structured probabilistic model, while using an LLM for language understanding and interaction. Our approach achieves competitive performance with much larger models in Agent-Agent play and, notably, is the first language agent to defeat human players in a controlled study - achieving a 67% win rate and receiving higher qualitative ratings than both reasoning baselines and human teammates. We release code, models, and a dataset to support future work on social reasoning in LLM agents, which can be found at https://camp-lab-purdue.github.io/bayesian-social-deduction/