Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
Quick Take
Harness self-evolution in LLM agents shows that models like Qwen3.5-9B can produce effective updates similar to Claude Opus 4.6, despite varying base capabilities. However, weak-tier models struggle to benefit from these updates, suggesting a focus on enhancing task-solving abilities over evolutionary capabilities.
Key Points
- Harness-updating shows flat performance across different model tiers.
- Qwen3.5-9B updates yield gains comparable to Claude Opus 4.6.
- Weak-tier models benefit little from updates due to activation failures.
- Mid-tier models gain the most from updated harnesses.
- Focus on task-solving capabilities rather than harness evolution in training.
Article Content
From source RSS / original summaryarXiv:2605. 30621v1 Announce Type: new 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 https://github. com/A-EVO-Lab/a-evolve/tree/release/harness-evolution.
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