研究人员开发了CANTANTE，一个旨在优化基于大型语言模型的多智能体系统配置的新框架。该系统通过将奖励分解为每个智能体的更新信号，解决了仅有系统级分数时分配性能功劳的挑战。CANTANTE在编程、数学推理和问答任务上进行了评估，与现有方法和未优化提示相比，它表现出更优越的性能，同时还降低了推理成本。 AI
影响 引入了一种优化多智能体LLM系统的新颖方法，有望提高复杂任务的性能和效率。
排序理由 该集群描述了一篇介绍用于优化基于LLM的多智能体系统的新颖框架的最新研究论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
Reinforcement learning from verifiable rewards (RLVR) is a promising paradigm for improving large language model (LLM) agents on long-horizon interactive tasks. However, in partially observable environments, incomplete observations cause agent beliefs to drift over time, while de…
LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet automating their configuration remains a structural challenge, as scores are available on…
LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet automating their configuration remains a structural challenge, as scores are available on…