Neuro-Symbolic Synergy for Interactive World Modeling

Understand

Large language models (LLMs) exhibit strong general-purpose reasoning capabilities, yet they frequently hallucinate when used as world models (WMs), where strict compliance with deterministic transition rules--particularly in corner cases--is essential.

• In contrast, Symbolic WMs provide logical consistency but lack semantic expressivity.
• To bridge this gap, we propose Neuro-Symbolic Synergy (NeSyS), a framework that integrates the probabilistic semantic priors of LLMs with executable symbolic rules to achieve both expressivity and robustness.
• NeSyS alternates training between the two models using trajectories inadequately explained by the other.