Learning Physics by Asking: Revisable Beliefs for Reconstructed 3D Assets

When is a reconstructed object's physical description sufficient for a robot task? We propose treating each asset as a persistent belief over physical parameters, with measurement provenance and a record of which tasks have been independently tested. For rigid tabletop objects with known geometry we study mass and contact-pair kinetic friction: visual hypotheses initialize the belief, task-conditioned probes revise it, and unidentifiable directions are retained rather than guessed. An exact four-hypothesis analysis shows that the probe with more parameter information can have zero value for the current task, and that successful sliding cannot establish readiness for lifting. A numerical pilot in a hidden-parameter reference environment (200 synthetic objects, no robot data) tests the resulting predictions: under a light-biased visual prior, five slides leave the mass interval uncalibrated while one task-selected lift restores near-nominal coverage, and task-risk selection reaches near-oracle lifting regret with about one contact where parameter information gain spends its first contact on friction. A language agent orchestrating the same tools is a matched-control research question, not a premise, and we specify the falsifiable evaluations that remain.