PIM: A Low-Latency Physical Intuition Model for Predicting Object Interaction Outcomes

Predicting object interaction outcomes from initial conditions is a central problem in data-driven physical reasoning. Existing methods often use rigid-body-level states or predict the evolution through autoregressive rollouts. Coarse representations cannot represent local deformation, while rollout errors may compound over long prediction horizons. Instead, we introduce PIM, a Physical Intuition Model that directly predicts a physical system's equilibrium state from its initial mesh geometry, kinematics, and physical attributes. PIM combines local k-nearest-neighbor (KNN) attention to encode intra-object geometry, anchor-based cross-object attention to model interactions, and a dual-branch decoder that predicts object-level translations and per-vertex residual displacements. Across MuJoCo scenarios involving both rigid and deformable interactions, PIM achieves lower vertex and centroid errors than the evaluated baselines. By predicting complete equilibrium states in a single forward pass, PIM substantially reduces latency relative to autoregressive learned models and MuJoCo rollouts under the evaluated protocols, enabling low-latency inference when only the final outcome is required.