Do stronger models always evolve harnesses better?

Self-evolving agents edit an external harness — prompts, skills, memories, tools — from execution evidence, without touching model parameters. The natural assumption is that stronger base models do this better on both ends. This paper disentangles two distinct capabilities and finds neither follows that assumption.

Harness-updating — producing persistent edits that lead to gains — is flat in base capability. Models across capability tiers produce updates yielding surprisingly similar gains; even a Qwen3.5-9B evolver induces gains comparable to Claude Opus 4.6. Writing a good skill or memory is apparently not bottlenecked by raw model strength.

Harness-benefit — actually improving when handed an updated harness — is non-monotonic. Weak-tier models gain little, mid-tier models benefit most, and strong-tier models benefit less than mid-tier. Two failure modes explain the weak end: failing to activate the relevant harness artifact, and failing to follow it faithfully once activated.

The practical inversion is sharp: invest capability budget in the agent that uses the harness, not the evolver that writes it — and target agent training at harness invocation and long-horizon instruction-following rather than at generating cleverer updates. This complicates the "let a frontier model improve everything" intuition and connects to Why do better reasoning models ignore instructions?: strong models may benefit less precisely because the bottleneck is faithful instruction-following, which scaling erodes.