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arXiv · 2610.06843

Recursive Video In-Context Learning for Agentic Robot

Wenrui Bao·Xinxin Liu·Bingxin Xu·Yuzhang Shang
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Abstract

LLM agents that orchestrate frozen vision-language-action (VLA) policies improve across episodes through text memory, which records what the agent did but not how the task is done. A demonstration video shows it, but fits poorly into an agent's context. The full video slows every turn, fixed keyframes lose the contact detail that decides whether a grasp holds, and what the agent needs shifts from the task's structure while planning to the frames around each contact. We introduce Recursive Video In-Context Learning (RV-ICL), a training-free method that turns a demonstration into a hierarchy the agent navigates rather than a prompt it receives. The hierarchy is built from the sub-events of the demonstration, such as grasps and releases. Its levels grow finer, from keyframes of the whole task to phases, moments and short clips, and are exposed through read-only tools. The agent reads the coarse levels before planning. During execution it re-enters the hierarchy whenever a step needs more detail and loads only the clip of its current sub-goal. One demonstration per task is enough. Built on RPent, RV-ICL raises success from 92.6% to 96.5% on LIBERO-PRO and from 86.7% to 95.8% on LIBERO-Plus.

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BibTeXRIS

Wenrui Bao, Xinxin Liu, Bingxin Xu, Yuzhang Shang. 2026-10-05. Recursive Video In-Context Learning for Agentic Robot. https://arxiv.org/abs/2610.06843

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