arXiv cs.CL· Phuong Tuan Dat, Qi Li, Xinchao Wang·· 2 天前AI 评分22
PRISM：基于特权同级图引导的多智能体拓扑设计
PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design
AI 导读
PRISM 通过特权信息学习，从同一执行验证图语料中恢复每个查询的偏好信号：教师模型经零初始化低秩分支读取一个部署时不可用的同级图，学生在自己采样的轨迹上匹配其逐决策目标。在六个基准上，PRISM 优于所有评估过的拓扑设计基线，匹配教师对照实验支持了特权查询信息的贡献。代码已开源。
正文 · 原文
Abstract:Multi-agent systems (MAS) based on large language models (LLMs) have become a powerful way to address complex problems, and their effectiveness depends critically on the collaboration topology that decides which agents participate and how they communicate. Recent work learns to generate this topology from the task query, fitting the generator to corpora of execution-verified graphs. The post-training method for such generators remains a limitation: a corpus lists the graphs that solved a query but never ranks them, leaving continued supervised fine-tuning (SFT) with no per-query preference to exploit. Without such a preference, the generator drifts toward the topologies the corpus contains most often rather than those that suit each query, so a gain on one task can come at a cost on another. We propose PRISM, which recovers per-query signal from the same corpus by learning with privileged information: a teacher reads, through a zero-initialized low-rank branch, a sibling graph that also solved the query and is unavailable at deployment, and the student matches its per-decision targets on trajectories the student samples itself. Across six diverse benchmarks PRISM improves over every topology-design baseline evaluated, and matched-teacher controls support the contribution of privileged query information. Code is available in this link.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20628 [cs.CL] |
| (or arXiv:2610.20628v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20628 arXiv-issued DOI via DataCite |
Submission history
From: Tuan Dat Phuong [view email]
[v1]
Tue, 29 Sep 2026 17:22:16 UTC (284 KB)
来源：arXiv cs.CL · arxiv.org