Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.
Reasoning over uncertain text by generative large language models
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CRISTAL is a neurosymbolic framework that synthesizes interpretable probabilistic world models from language priors for full Bayesian analysis and budget-aware data acquisition, claiming Bayes-optimal accuracy on synthetic equity classification with 5 examples.
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Bayesian Social Deduction with Graph-Informed Language Models
Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.
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The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
CRISTAL is a neurosymbolic framework that synthesizes interpretable probabilistic world models from language priors for full Bayesian analysis and budget-aware data acquisition, claiming Bayes-optimal accuracy on synthetic equity classification with 5 examples.