SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems
The paper introduces SAGE, a framework for evaluating socialized evolution in agent ecosystems. It compares two conditions: agents evolving with peer history and those relying solely on self-improvement. Findings indicate that while peer history can enhance performance, its benefits are context-dependent and vary by agent and arena.
- ▪SAGE evaluates agents in two conditions: SocialEvo with peer history and SelfEvo with only self-history.
- ▪The study finds that agents can achieve breakthroughs with peer experience when they plateau under self-improvement.
- ▪Social gains from peer history depend on the ability to abstract knowledge rather than just the volume of exposure.
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Computer Science > Artificial Intelligence arXiv:2606.03544 (cs) [Submitted on 2 Jun 2026] Title:SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems Authors:Linyue Pan, Yaoming Zhu, Lin Qiu, Xuezhi Cao, Xunliang Cai View a PDF of the paper titled SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems, by Linyue Pan and 4 other authors View PDF HTML (experimental) Abstract:Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior. Yet agents increasingly operate alongside peers whose strategies and outcomes are publicly visible.
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