Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task execution without changing model parameters. Harness self-evolution adapts such agents by updating these harnesses from executi...
ArXiv CS.AI
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This research reveals that current LLM agents' ability to improve their 'harnesses' (prompts, tools) doesn't scale with their base capabilities, and surprisingly, weaker models might benefit more from these self-evolved harnesses. This suggests a need to rethink how we enhance LLM agent performance, focusing on improving the core agent's ability to utilize learned knowledge rather than just its capacity to generate updates.
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