# Context-Aware Adaptive SMC Resampling (CAR-SMC)

Psychological prediction must say both what a character will do and what it believes, yet models tuned on surface patterns often return action and belief answers that disagree. A neuro-symbolic reasoning framework splits the latent cognitive state into a belief representation and an executable program representation, filters hypotheses that violate contextual constraints through energy-penalized particle resampling, and lets the two pathways update separately in stop-gradient alternating fixed-point iterations. Under the text-based classification setting on OpenToM and FANToM, the share of samples with both action and belief correct reaches 70.8% and 52.1%, above scale-matched pure encoder classifiers.

Psychological prediction must say both what a character will do and what it believes, yet models tuned on surface patterns often return action and belief answers that disagree. A neuro-symbolic reasoning framework splits the latent cognitive state into a belief representation and an executable program representation, filters hypotheses that violate contextual constraints through energy-penalized particle resampling, and lets the two pathways update separately in stop-gradient alternating fixed-point iterations. Under the text-based classification setting on OpenToM and FANToM, the share of samples with both action and belief correct reaches 70.8% and 52.1%, above scale-matched pure encoder classifiers.