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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03544 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | O4dHLi8pa4eV |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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