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Research PaperResearchia:202602.05020

DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching

Yuxing Lu

Abstract

Multi-agent systems built from prompted large language models can improve multi-round reasoning, yet most existing pipelines rely on fixed, trajectory-wide communication patterns that are poorly matched to the stage-dependent needs of iterative problem solving. We introduce DyTopo, a manager-guided multi-agent framework that reconstructs a sparse directed communication graph at each round. Conditioned on the manager's round goal, each agent outputs lightweight natural-language query (need) and \...

Submitted: February 5, 2026Subjects: AI; Artificial Intelligence

Description / Details

Multi-agent systems built from prompted large language models can improve multi-round reasoning, yet most existing pipelines rely on fixed, trajectory-wide communication patterns that are poorly matched to the stage-dependent needs of iterative problem solving. We introduce DyTopo, a manager-guided multi-agent framework that reconstructs a sparse directed communication graph at each round. Conditioned on the manager's round goal, each agent outputs lightweight natural-language query (need) and \key (offer) descriptors; DyTopo embeds these descriptors and performs semantic matching, routing private messages only along the induced edges. Across code generation and mathematical reasoning benchmarks and four LLM backbones, DyTopo consistently outperforms over the strongest baseline (avg. +6.2). Beyond accuracy, DyTopo yields an interpretable coordination trace via the evolving graphs, enabling qualitative inspection of how communication pathways reconfigure across rounds.


Source: arXiv:2602.06039v1 - http://arxiv.org/abs/2602.06039v1 PDF: https://arxiv.org/pdf/2602.06039v1 Original Article: View on arXiv

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Date:
Feb 5, 2026
Topic:
Artificial Intelligence
Area:
AI
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