MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention

Theory-of-Mind (ToM) reasoning enables embodied agents to understand human beliefs, goals, and intentions, but existing benchmarks mainly evaluate this ability through offline question answering or scenario-level action prediction. MindPower advances embodied ToM by introducing robot-centric reasoning from perception to action; however, it does not evaluate whether an agent can continuously interact with a changing environment and intervene only when assistance is needed. Building on MindPower, we introduce the MindHelper Challenge, which extends embodied ToM evaluation to real-time closed-loop precision intervention. An agent must continuously observe the environment, maintain actor-specific beliefs, identify when a human requires assistance, generate executable actions, and remain silent when intervention is unnecessary. We further propose MindClaw, a simple yet effective Claw-style framework that integrates an actor-specific Belief Table, embodied cognitive skills, and a Trigger-based cognitive dispatcher. Experiments show that MindClaw achieves 36.63% precise intervention rate and 14.36% task accuracy, substantially outperforming direct VLM baselines, whose corresponding results remain below 12.05% and 3.80%.