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Robots can't truly improve themselves yet; current systems lack the crucial ability to recursively refine their own learning processes based on physical experience.
Claw-like agents are vulnerable to severe security breaches, with malicious plugins achieving a 100% success rate in attacks.
Explicitly training LLMs to verbalize confidence scores and signal reasoning-time uncertainty unlocks better calibration, failure detection, and control in retrieval-augmented generation.