A new study examines whether coding agents can undergo continual harness self-evolution without forgetting previously learned capabilities, focusing on approaches that improve agent performance without modifying the foundation model's parameters. The research finds that existing methods largely study agent evolution in ways that leave the forgetting question unresolved.
Recently, harness self-evolution has emerged as an important paradigm for improving agent performance. Prior studies show that evolving the agent harness can effectively enhance agent capabilities without modifying the foundation model's parameters. We find that existing approaches largely study age
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