Rethinking Self-Evolving Agents: Do We Still Need Prescribed Optimization Pipelines?

TL;DR - An arXiv study asks whether self-evolving agents still need hand-designed optimization pipelines when a frontier model is the optimizer, and finds that an unconstrained "Open-Ended Optimization" (OEO) setup mostly beats prescribed pipelines — but only above a capability threshold.

• OEO fixes the objective, permitted interactions, budget, data boundary, and evaluation, but lets the optimizer compose the improvement process online, versus SkillOpt (staged pipeline, bounded edits) and GEPA (reflective evolutionary search).
• Across 14 head-to-head comparisons over 8 benchmark-target-model settings, GPT-5.5-driven OEO scored 12 wins, 1 tie, and 1 loss of 0.21 pp, while using a median 34.3% of SkillOpt's configured target-interaction token budget.
• Gains aren't just a prior-driven rewrite: a one-shot, zero-interaction control fails to explain them.
• Delegation has a capability boundary — SkillOpt wins with a medium optimizer, and a weak optimizer can't operate the unchanged OEO interface; trajectory analysis shows prescription alters how optimization proceeds more consistently than final behavior.