arXiv cs.AI· Ben Rachmut, Ning Zhang, Yevgeniy Vorobeychik, William Yeoh·· 12 小时前AI 评分35
用符号引导调控 LLM 智能体自主性，提升分布式多智能体协调表现
Symbolic Guidance for LLM Agents in Distributed Multiagent Coordination
AI 导读
研究提出符号引导分类法（SGT），按从开放式自然语言推理到完全规定算法执行的谱系刻画 LLM 智能体的自主性等级。基于 AgentsNet 基准的测试显示，提供部分伪代码引导的中间自主性等级，表现稳定优于无引导智能体和完全规定式规范。该结果表明自主性调控是基于 LLM 的分布式协调的关键设计原则。
正文 · 原文
Abstract:Large language models (LLMs) are increasingly deployed as autonomous agents in multi-agent systems, yet their ability to reliably execute distributed coordination protocols remains poorly understood. While AgentsNet, a benchmark framework for distributed coordination among LLM agents, enables such coordination, granting full reasoning autonomy often leads to inconsistent or degraded performance in complex domains.
We hypothesize that coordination can be improved by regulating agent autonomy through symbolic guidance derived from established algorithms. To investigate this, we introduce the \emph{Symbolic Guidance Taxonomy (SGT)}, which characterizes a spectrum of autonomy ranging from open-ended natural language reasoning to fully prescribed algorithmic execution, with intermediate levels providing partial pseudocode guidance. Our results show that intermediate autonomy levels consistently outperform both unguided agents and fully prescriptive specifications. These findings identify autonomy regulation as a key design principle for LLM-based distributed coordination.
| Comments: | A preliminary version of this work was published as an extended abstract in the Proceedings of AAMAS 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.31963 [cs.AI] |
| (or arXiv:2609.31963v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.31963 arXiv-issued DOI via DataCite |
Submission history
From: Ben Rachmut [view email]
[v1]
Fri, 25 Sep 2026 20:07:57 UTC (291 KB)
[v2]
Thu, 22 Oct 2026 18:20:41 UTC (293 KB)
来源：arXiv cs.AI · arxiv.org