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

2026-08-10

AI Summary

This work addresses the limitations of traditional self-evolving agents, which rely on predefined optimization pipelines that constrain large language models’ ability to autonomously plan improvement trajectories. The authors propose an Open-Ended Optimization (OEO) framework that, under fixed objectives, interaction boundaries, and resource budgets, enables optimizers to dynamically construct their own refinement processes online, redefining rigid pipelines as optional capability scaffolds. For the first time, they demonstrate that a state-of-the-art model (GPT-5.5) can autonomously accomplish complex optimization tasks without any preset workflow. Across 14 comparisons spanning eight benchmark–target model configurations, OEO achieves 12 wins, 1 tie, and 1 loss, while using only a median of 34.3% of the interaction token budget required by SkillOpt, substantially improving both efficiency and performance.

Abstract

Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop. We ask whether this task-specific procedure remains necessary when a frontier model acts as the optimizer. We introduce Open-Ended Optimization (OEO), which keeps the objective, permitted interactions, resource budget, data boundary, and evaluation fixed while allowing the optimizer to compose the improvement process online. We compare OEO with two complementary prescribed approaches: SkillOpt, a staged pipeline with bounded edits, and GEPA, a reflective evolutionary search. Across 14 head-to-head comparisons over 8 benchmark-target-model settings, GPT-5.5-driven OEO records 12 wins, 1 tie, and 1 narrow loss of 0.21 percentage points. It uses a median 34.3 percent of SkillOpt's configured target-interaction token budget. A one-shot, zero-interaction control shows that the gains are not explained by a single prior-driven rewrite. However, delegation has a capability boundary: SkillOpt outperforms OEO with a medium optimizer, and a weak optimizer cannot operate through the unchanged OEO interface. In the fully instrumented OEO-SkillOpt pair, trajectory analysis further shows that prescription changes how optimization proceeds more consistently than it changes final behavior. Together, these findings recast prescribed pipelines as capability-dependent scaffolding: essential constraints remain external, but a sufficiently capable optimizer can compose the route from measurable feedback to persistent improvement.

Problem

• self-evolving agents
• prescribed optimization pipelines
• Open-Ended Optimization
• frontier models
• optimization process

Innovation

• Open-Ended Optimization
• Self-Evolving Agents
• Prescribed Optimization Pipelines
• Frontier Language Models
• Autonomous Improvement