From Sequence Prediction to Mechanism Execution: The Neural Mechanism Interpreter for World Modeling

The prevailing paradigm of world modeling relies on Transformer or state-space model (SSM) backbones, which conceptualize physical dynamics as a dense sequence prediction problem. However, this global/recurrent sequence matching inextricably entangles entity state representations with transition rules, leading to catastrophic forgetting and a lack of combinatorial generalization when environmental dynamics non-stationarily mutate. In this paper, we propose a fundamental architectural departure: the Neural Mechanism Interpreter (NMI). Instead of context matching over monolithic sequences, NMI maintains a dynamic continuous state and executes a discrete, discoverable library of localized neural mechanisms. We introduce Dynamic Mechanism Routing (DMR) to sparsely assign state-transition programs to specific objects, and Targeted Mechanism Revision (TMR), a novel backpropagation protocol that isolates gradient updates exclusively to active mechanisms during prediction failures. This freezes unrelated mechanism parameters, reducing a direct source of catastrophic forgetting. Extensive experiments on complex, rule-mutating interactive physical environments and real-world pedestrian forecasting datasets demonstrate that NMI achieves zero-shot combinatorial generalization and cross-domain transfer significantly beyond Transformer and SSM baselines. Crucially, under sudden interventions of physical laws, NMI adapts and recovers prediction accuracy with minimal few-shot interactions, supporting its viability as a principled architectural foundation for causal-driven interactive models.