Self-Evolving Agent Harness

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Abstract:Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We characterize search through evolutionary optimization and examine its reliability through continual PAC-learning. We examine how persistent harness revisions affect knowledge sharing and organizational workflows, with implications for scientific reliability, physical safety, and institutional accountability.

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