LLMs can reason about human behavior more accurately by explicitly modeling beliefs as interconnected, time-varying graphs rather than static states—especially important for high-stakes domains like emergency response.
This paper improves how large language models reason about what people believe and why they act. Instead of treating beliefs as fixed, the authors model beliefs as a dynamic graph that changes over time, showing how new information updates what people think and how that shapes their decisions. They test this on disaster evacuation scenarios where understanding evolving beliefs is critical.