Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models
A calibrated stochastic world model can reveal how uncertain a future is without revealing why it branches. The same conditional future law can arise because an observation aliases physical states or because dynamics remain random after the declared full state is fixed. We prove that ordinary transitions cannot identify these two sources, even for a perfect probabilistic predictor. ClosurePairs makes them identifiable by crossing compatible microstates with repeated exogenous disturbances and estimating state, noise, and state-noise interaction variance. The central consequence is operational: under finite hierarchical sampling, forecast difficulty governs the useful compute scale, while ...
| Authors | Yibin Dong |
|---|---|
| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 8 August 2026 |
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