AIBullisharXiv – CS AI · Mar 56/10
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Learning Physical Principles from Interaction: Self-Evolving Planning via Test-Time Memory
Researchers introduce PhysMem, a memory framework that enables vision-language model robot planners to learn physical principles through real-time interaction without updating model parameters. The system records experiences, generates hypotheses, and verifies them before application, achieving 76% success on brick insertion tasks compared to 23% for direct experience retrieval.