COORDINATEWISE PRECISION ADAPTATION FOR FROZEN WORLD MODELS

Local sensor faults change the reliability of observations used to infer a world model's latent state. We combine a calibrated diagonal base with causal innovation statistics for coordinatewise precision adaptation on frozen world models. We evaluate forty predictive coding world model (PCWM) and Gaussian recurrent state-space model (RSSM) checkpoints in two low-dimensional environments. Under local variance faults, one-step error decreases by 5.56–17.81% relative to independently calibrated static weights. Same-base controls retain 6.29–15.43% improvements on PCWM. A prospective comparison gives static and adaptive refinement the same filtering-time cap and four optimizer families, keeping their precision calibration fixed. Validation-selected adaptive configurations reduce local-fault error by 5.60–24.90% across all four settings relative to validation-selected static configurations. A separate multistep development evaluation yields 5.3–22.1% lower fault-condition error than static refinement at five transitions, with gains persisting at twenty transitions. These findings support coordinatewise state refinement for frozen world models under localized sensor corruption.