arXiv cs.AI· 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·· 3 天前AI 评分24
CoEvoke：通过扰动与引导激发多智能体协作轨迹收集
CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection
AI 导读
CoEvoke 是一个跨域框架，通过可控扰动制造可恢复的困难、并用受限参考引导调查，在轨迹收集阶段激发多智能体的检查、反馈与修复交互。该数据集包含 4,130 个软件工程与推理任务上的 4,567 条可重建轨迹；在 2,287 对同任务实验中，七个扰动组的反馈提升达 47.72-100 个百分点。
正文 · 原文
Authors: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
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 cross-domain framework that evokes these interactions during trajectory collection. Controlled perturbations create recoverable difficulties, while restricted reference-based guidance directs investigation. The resulting dataset contains 4,567 reconstructable trajectories over 4,130 software-engineering and reasoning tasks, linking public interactions to evidence, decisions, and task-native outcomes. Separate experiments over 2,287 same-task pairs show feedback increases of 47.72-100 percentage points across seven perturbation groups; guidance has smaller, model-dependent effects. With identical training tasks, decision-record counts, and updates, perturbation-enriched judge training improves ACPBench-Hard success from 35.38% to 42.12% (+6.73 points). SWE verifier training gains 7.60-9.40 points over interface-matched untrained references. Workflow and post-training evaluations preserve task-solving topology with unperturbed inputs and no private-reference guidance. These results support designing collection conditions to obtain corrective supervision that improves collaborative decisions with frozen solving agents.
| Comments: | 14 pages, 4 figures, 3 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.14021 [cs.LG] |
| (or arXiv:2610.14021v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14021 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lin Wu [view email]
[v1]
Tue, 29 Sep 2026 13:54:06 UTC (1,478 KB)
来源：arXiv cs.AI · arxiv.org