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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

Portuguese

Este estudo introduz o GuideEvolve, um quadro de agentes auto-evolutivos que incorpora orientação especializada e três mecanismos regulatórios autônomos, coletivamente denominados os 3Ts: Think Fast and Slow, Throwback, e Termination por escolha. O quadro aborda as limitações críticas nos paradigmas de agentes auto-evolutivos existentes através de duas inovações: (1) um pipeline evolutivo orientado por peritos específicos para tarefas e (2) a integração dos mecanismos 3Ts para facilitar a otimização cognitiva, a recuperação de desempenho através de rollback temporal e protocolos de terminação adaptativa. A validação empírica em todos os paradigmas de agentes auto-evolutivos do MGSM e MMLU demonstra que o GuideEvolve métricas de desempenhoque são comparáveis aos

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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