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GuideEvolve: Self-Evolving Agent Framework with Expert Guidance and 3Ts Mechanisms

美虹 賈, Yasemin Timar, Jonathan Taws · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30017 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

该研究介绍了GuideEvolve,一个自我演变的代理框架,包括专家指导和三个自主监管机制,共同称之为3Ts: Think Fast and Slow, Throwback, and Termination by choice. 该框架通过双重创新解决了现有的自我演变代理范式的关键限制:(1)一个任务特定的专家评估驱动的进化管道,以及(2)3T的机制的整合,以促进认知优化,通过时效回收和适应性终止协议的恢复。 在MGSM和MMLU基准中,经验验证证明GuideEvolve实现了与当前最先进的模型相比的性能指标(89.8%的精度与GödelAgent在MGSM上的90.6%的精度;89.1%与MMLU的87.9%相比),同时显示出显著

Machine translation (Meta M2M100); the English original follows.

Abstract

This study introduces GuideEvolve, a self-evolving agent framework incorporating expert guidance and three autonomous regulatory mechanisms, collectively termed the 3Ts: Think Fast and Slow, Throwback, and Termination by choice. The framework addresses critical limitations in extant self-evolving agent paradigms through dual innovations: (1) a task-specific expert-evaluation-driven evolutionary pipeline, and (2) the integration of 3Ts mechanisms to facilitate cognitive optimization, performance recovery via temporal rollback, and adaptive termination protocols. Empirical validation across the MGSM and MMLU benchmarks demonstrates that GuideEvolve attains performance metrics comparable to current state-of-the-art models (89.8% accuracy versus Gödel Agent's 90.6% on MGSM; 89.1% versus 87.9% on MMLU), while exhibiting notably reduced variance (25-59%) and enhanced robustness. Furthermore, the framework achieves a 100% evolution success rate, superior to the baseline's 82%, diminishes required optimization iterations by approximately 50% (from 30 iterations in baseline to 15.1 and 13.4 across tasks), and provides improved stability for production deployment.

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Topics: Computer science, Extant taxon, Variance (accounting), Dual (grammatical number), Baseline (sea), Artificial intelligence

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