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Even state-of-the-art VLMs exhibit systematic failures in reasoning about the physical feasibility of actions in 3D environments, despite high semantic confidence.
A confidence-based gating mechanism lets a 14B parameter reward model outperform 70B parameter models, achieving a new accuracy-efficiency Pareto frontier.
