CORAL：迈向开放探索的自主多智能体进化

CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

April 2, 2026

作者: Ao Qu, Han Zheng, Zijian Zhou, Yihao Yan, Yihong Tang, Shao Yong Ong, Fenglu Hong, Kaichen Zhou, Chonghe Jiang, Minwei Kong, Jiacheng Zhu, Xuan Jiang, Sirui Li, Cathy Wu, Bryan Kian Hsiang Low, Jinhua Zhao, Paul Pu Liang

cs.AI

摘要

基于大语言模型（LLM）的进化方法为开放式发现提供了前景广阔的路径——这类探索需要持续的知识积累与搜索机制。现有方法仍严重依赖固定启发式规则与硬编码的探索策略，限制了LLM智能体的自主性。我们提出CORAL，首个面向开放式问题的自主多智能体进化框架。该框架通过共享持久化内存、异步多智能体执行和基于心跳信号的干预机制，使长期运行的智能体能够自主探索、反思与协作，从而取代僵化控制。CORAL还提供隔离工作区、评估器分离、资源管理、智能体会话与健康管理等实用保障机制。在数学、算法和系统优化等多样化任务上的实验表明，CORAL在10项任务中刷新了最优性能记录，相较于固定进化搜索基线，仅需极少的评估次数即可实现3-10倍的提升幅度。在Anthropic内核工程任务中，四个协同进化的智能体将已知最佳成绩从1363周期提升至1103周期。机理分析进一步揭示了知识复用、多智能体探索与通信对性能增益的贡献。这些结果表明，增强智能体自主性与多智能体协同进化能显著推动开放式发现进程。代码已开源：https://github.com/Human-Agent-Society/CORAL。

English

Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard-coded exploration rules, which limit the autonomy of LLM agents. We present CORAL, the first framework for autonomous multi-agent evolution on open-ended problems. CORAL replaces rigid control with long-running agents that explore, reflect, and collaborate through shared persistent memory, asynchronous multi-agent execution, and heartbeat-based interventions. It also provides practical safeguards, including isolated workspaces, evaluator separation, resource management, and agent session and health management. Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks. On Anthropic's kernel engineering task, four co-evolving agents improve the best known score from 1363 to 1103 cycles. Mechanistic analyses further show how these gains arise from knowledge reuse and multi-agent exploration and communication. Together, these results suggest that greater agent autonomy and multi-agent evolution can substantially improve open-ended discovery. Code is available at https://github.com/Human-Agent-Society/CORAL.