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New CoEvoke framework enhances multi-agent collaboration training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CoEvoke, a new framework designed to improve multi-agent collaboration training by collecting more informative trajectories. This framework introduces controlled perturbations and reference-based guidance to encourage agents to engage in checking, feedback, and repair processes during task completion. The resulting dataset, comprising 4,567 trajectories across software engineering and reasoning tasks, demonstrates that this enriched training data significantly enhances agent performance on benchmarks like ACPBench-Hard and SWE verifier tasks. AI

IMPACT CoEvoke's approach to collecting corrective supervision could lead to more robust and capable multi-agent systems.

RANK_REASON The cluster contains a research paper detailing a new framework for AI training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CoEvoke framework enhances multi-agent collaboration training

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The cluster contains a research paper detailing a new framework for AI training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lin Wu, Shu Yao, Yuheng Wang, Jingru Fan, Huatao Li, Yihan Li, Liu Yixuan, Cheng Liu, Jian Chang, Zeyu Zhao, Aohua Zhang, Huiying Zhao, Shuyuan Zhu, Chen Qian ·

    CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection

    arXiv:2610.14021v1 Announce Type: cross Abstract: Training multi-agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval or repeat unsuccessful attempts without useful correction. We introduce CoEvoke, a…

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