Surgical Meta-Optimization for Agent Evolution with Minimal Samples

The transition of Large Language Models (LLMs) into autonomous agents requires ways to improve an agent after it is built, without retraining the backbone. However, prevailing paradigms are largely trapped in a brute-force cycle, relying on massive computational resources, extensive annotated data, and unbounded search spaces. This resource-heavy approach hits a strict ”resource wall” in practical, locally deployed scenarios. To address the challenge of extreme compute and data starvation, we propose a frugal offline agent-evolution framework driven by structure-aware surgical intervention. Rather than pursuing computationally prohibitive full-scale global optimization, we restrict the search to two components of the agent—its system prompt and its controller logic—that can be edited and verified without touching model weights or tool APIs. This is achieved through a two-stage meta-optimization in which each stage optimizes one component independently while holding the other fixed at its baseline. Stage I employs a synergistic “Thought-Prompt” co-evolution to rapidly discover a compact, transferable reasoning mindset. Stage II utilizes a contrastive reflection-guided process to independently harden the programmatic execution loop against structural brittleness. Remarkably, operating under a strict budget of merely around 140 candidate evaluations and 30 sparse evaluation samples, our evolved agent successfully tackles expert-level, unseen tasks. Extensive evaluations on exceptionally challenging benchmarks, including Humanity's Last Exam (HLE), demonstrate that strategic, localized component optimization enables stable generalization without requiring massive data or heavy training dependencies. Code is available at Supplement.