A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration

• URL: http://arxiv.org/abs/2608.08689v1
• Date: Sun, 09 Aug 2026 13:11:40 GMT
• Title: A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration
• Abstract summary: We propose Structural Dynamics Graph World Model as an executable backbone contract.<n>An optional bounded residual R concentrates learnability, while a global projection maps states to feasibility.<n>On a semi-synthetic floodbed and USGS streamflow, SD-GWM reduces constraint violations to floating-point tolerance.
• Score: 9.354331831290233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The state evolution of a complex system arises jointly from object laws, relational propagation, domain conservation, and unmodeled error. Forcing all sources into one black box makes mechanism attribution and constraint preservation unauditable; forcing every mechanism into one equation family discards mature domain solvers. We propose SD-GWM, a Structural Dynamics Graph World Model as an executable structural contract: nodes declare self-dynamics S, edges declare neighbor graph-coupled dynamics N---both fixed-form mechanism assets (rules, ODEs, solvers) calibrating only authorized parameters. An optional bounded residual R concentrates learnability, while a global projection maps states to feasibility, enforcing constraints without guaranteeing accuracy gains. On eight pre-registered research questions, SD-GWM delivers (i) heterogeneous integration: rules and solvers plug in natively; (ii) semantic fidelity: disabling R preserves source semantics bit-for-bit, with four theory properties under explicit proof/empirical boundaries; (iii) auditable governance: stepwise traces enable counterfactual fault localization (top-1 = 1.0) without post-hoc approximations. On a semi-synthetic flood testbed and USGS streamflow, SD-GWM reduces constraint violations to floating-point tolerance in analytical tests and to zero in semi-synthetic and real-data cases. Persistence matches SD-GWM in calm periods, but during a 254-day extreme-flood shift persistence and all neural baselines collapse (90-min RMSE 892-3007 cfs) while SD-GWM holds at 108 cfs (8-28x gain). The bounded residual cuts RMSE ~50% only under backbone bias. We position SD-GWM not as a universally superior forecaster, but as a verifiable substrate for auditable, constraint-safe spatiotemporal mining.

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