Paper: Do Frontier Models Eliminate the Need for Prescribed Pipelines in Self-Evolving Agents?
burny_tech · x · 2026-08-11
A new arXiv paper, Rethinking Self-Evolving Agents, investigates whether self-evolving agents still require prescribed optimization pipelines when a frontier model acts as the optimizer.
The authors introduce Open-Ended Optimization (OEO), which keeps the objective, interaction boundaries, and evaluation fixed while allowing the model to dynamically compose the improvement process. Experiments show that across 14 head-to-head comparisons, GPT-5.5-driven OEO achieved 12 wins, 1 tie, and 1 narrow loss, while using only a median of 34.3% of the token budget required by baseline methods.
However, the paper identifies a capability boundary for this delegation: when using a medium-capability model, the prescribed pipeline (SkillOpt) performs better, and weak models cannot operate through the OEO interface at all.
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