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This work presents S4R (Scaling for Rigid-Body Interpenetration Resolution), a scale-continuation method for static interpenetration repair, and evaluates S4R on Kubric, HY3D-Bench, and Thingi10K using a shared mesh-level evaluator and a unified per-scene timing protocol.
Findings reveal sustained, dependency-consistent evidence integration, rather than isolated fact retrieval, as a key bottleneck for current deep research agents.
NeuralActuator not only predicts actuator dynamics but also enhances sensorless force perception, significantly reducing sim-to-real errors in low-cost robotics.
