Disentangling Observation–System Coupling in Physical Reconstruction via Active Sensing and Intervention

Physical reconstruction for industrial diagnosis, embodied interaction, and physical twins aims to recover object-specific properties that support prediction under new physical inputs. Existing methods often rely on a fixed passive protocol, where the acquisition scheme is predetermined rather than adapted to the observed object. However, distinct physical systems can remain compatible with the same measurements because intrinsic physical effects and observation effects are entangled. In this paper, we propose the PARAD (Physics-Aware Reconstruction through Active Disentanglement) framework, which models physical reconstruction through active sensing and intervention and separates physical response generation from measurement formation to analyze such ambiguities. Specifically, PARAD maintains a joint belief over physical and observation parameters from accumulated evidence, and sequentially selects intervention–sensing pairs to distinguish unresolved, task-relevant physical alternatives. Experiments across thermal, vibration, and wave-scattering systems show improved prediction under new inputs, transfer of informative evidence to an unchanged inverse solver. We also propose a foundation for physical reconstruction that actively seeks the evidence needed for prediction, interpretation, and verification.