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Published a review paper on recursive self-improvement in physical AI

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Meng Luo and other researchers published a 93-page review paper, "Toward Recursive Self-Improvement in Physical AI" (arXiv:2610.29677), on arXiv. The paper situates recursive self-improvement (RSI) within the robot learning framework, outlines paths from improving robot capabilities to improving the processes that produce those capabilities, and proposes evaluation principles and research directions. The paper notes that existing research concentrates on manipulation tasks and supports three types of effects: behavior improvement, gains for subsequent learning, and process improvement; some systems also change the improver's own knowledge or tools, but the physical research reviewed has not yet isolated gains attributable to recursion. For benefits to persist, conditions must be met: feedback must target remaining learning needs, the recipient must be able to use the changes, value must be retained after task or hardware changes, and continued practice must be safe and affordable.