Symbolic Guidance for LLM Agents in Distributed Multiagent Coordination
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 ope...
| Authors | Ben Rachmut, Ning Zhang, Yevgeniy Vorobeychik et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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