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Prompt-based agent memory inevitably saturates the context window, but alternating fast in-context skill distillation with slow parametric consolidation turns ephemeral interaction traces into durable model competence.
A new adaptive control law reduces tracking error chattering by over 30% in cable-driven robots, enhancing performance in dynamic environments.
