Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
LLM agents adapt by updating external harnesses (prompts, skills, memories, tools), but it's unclear if base task-solving ability predicts harness self-evolution capability. The study shows harness-updating is flat across models, while harness-benefit is non-monotonic: mid-tier models benefit most, with weak and strong tiers benefiting less. Recommendations include investing in the task-solving agent over the evolver and focusing on harness invocation and long-horizon instruction following.
[2605.30621] Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
[Submitted on 28 May 2026]
Title:Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
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Abstract: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 execution evidence. Yet it remains unclear whether a model's base capability in task-solving predicts its capabilities in harness self-evolution: which models produce useful harness updates, and which actually benefit from them? We analyze two harness self-evolution capabilities: (i) harness-updating, the capability to produce useful persistent harness updates from execution evidence; (ii) harness-benefit, the capability to benefit from updated harnesses during task solving. Our analysis reveals two findings. First, harness-updating is flat in base capability: models from different capability tiers produce harness updates that lead to surprisingly similar gains; even Qwen3.5-9B's updates yield gains comparable to those of Claude Opus~4.6. Second, harness-benefit is non-monotonic in base capability: weak-tier models benefit little from updated harnesses, mid-tier models benefit most, and strong-tier models benefit less than mid-tier. We trace low gains at the weak tier to two failure modes: weak-tier models may fail to activate relevant harness artifacts, or activate them but fail to follow them faithfully. These findings suggest investing capability budget in the task-solving agent rather than the evolver, and targeting harness invocation and long-horizon instruction following in agent training. Our source code is publicly available at this https URL.
Comments: 24 pages, 9 figures, 12 tables
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.30621 [cs.AI]
(or arXiv:2605.30621v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.30621
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Minhua Lin [view email] [v1] Thu, 28 May 2026 22:16:14 UTC (1,619 KB)
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