arXiv AI
5d ago
Self-Evolving Agent Harness
arXiv:2610.18813v1 Announce Type: new Abstract: Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model we...
Self-Evolving Agent Harness
The paper introduces the Self‑Evolving Agent Harness, a framework that allows an agent’s software scaffolding—prompts, memory, workflows, and executable components—to evolve while keeping the underlying model weights unchanged. It formalizes a feedback‑driven process where accepted revisions persist across runs, separating update targets from proposal and admission mechanisms, and distinguishes changes to interpreted state from revisions to tools and code. The authors analyze evolutionary search, provide generalization and retention bounds under PAC‑learning assumptions, and discuss the impact of persistent harness revisions on knowledge sharing, organizational workflows, and broader implications for scientific reliability, safety, and accountability.
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