A framework combining LLM policy interpretation with a physically conserved graph-latent world model and uncertainty-separated learning achieves 33% higher rationale consistency and 82.3% operability on a 10-node semiconductor benchmark under perturbations.
Learning from trials and errors: Reflective test-time planning for embodied LLMs
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abstract
Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequence of independent trials where mistakes repeat rather than accumulate into experience. Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: \textit{reflection-in-action}, where the agent uses test-time scaling to generate and score multiple candidate actions using internal reflections before execution; and \textit{reflection-on-action}, which uses test-time training to update both its internal reflection model and its action policy based on external reflections after execution. We also include retrospective reflection, allowing the agent to re-evaluate earlier decisions and perform model updates with hindsight for proper long-horizon credit assignment. Experiments on our newly-designed Long-Horizon Household benchmark and MuJoCo Cupboard Fitting benchmark show significant gains over baseline models, with zero-shot generalization to photorealistic HM3D environments and real-robot experiments on a Franka Panda arm. Ablations confirm that reflection-in-action and reflection-on-action are mutually dependent, and that retrospective reflection achieves better credit assignment than step-wise external feedback at lower computational overhead. Qualitative analyses further highlight behavioral correction through reflection.
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AdaJEPA performs closed-loop test-time adaptation of latent world models during MPC by executing an action chunk, observing the transition, and taking one gradient step on the model before replanning, yielding higher goal-reaching success.
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A framework combining LLM policy interpretation with a physically conserved graph-latent world model and uncertainty-separated learning achieves 33% higher rationale consistency and 82.3% operability on a 10-node semiconductor benchmark under perturbations.
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