DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching
On the map: LLM reasoning → Reasoning for agents, planning and code
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 12 of its 26 references that are in the map. First posted on arXiv 5 Feb 2026.
Builds on
12 of 26 references.
References · 12
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Built on by
Citing papers per year: 2026: 8.
8 papers.
- 2026Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspectivecited by 4
- 2026The Institutional Scaling Law: Non-Monotonic Fitness, Capability-Trust Divergence, and Symbiogenetic Scaling in Generative AIcited by 1
- 2026Shared Prefixes, Better Credit: Adaptive Routing for Multi-Agent Reasoningcited by 1
- 2026Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligencecited by 1
- 2026One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Predictioncited by 0
- 2026Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariantscited by 0
- 2026What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Frameworkcited by 0
- 2026Proxifield: Decentralized Multi-Agent Communication through Semantic Proximitycited by 0
- 2026Proxifield: Decentralized Multi-Agent Communication through Semantic Proximity16 Sep 2026
- 2026What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Framework11 Sep 2026
- 2026Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence21 Aug 2026
- 2026Shared Prefixes, Better Credit: Adaptive Routing for Multi-Agent Reasoning3 Aug 2026
- 2026Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective10 Jun 2026
- 2026Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants4 Jun 2026
- 2026One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction31 Mar 2026
- 2026The Institutional Scaling Law: Non-Monotonic Fitness, Capability-Trust Divergence, and Symbiogenetic Scaling in Generative AI14 Mar 2026
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- 2026Inference-Time Graph Engineering for Multi-Agent LLM Workflows
- 2025AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
- 2026G2CP: A Graph-Grounded Communication Protocol for Verifiable and Efficient Multi-Agent Reasoning
- 2025AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
- 2025Benefits and Limitations of Communication in Multi-Agent Reasoning
- 2026Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
- 2025Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
- 2026Brain-Inspired Graph Multi-Agent Systems for LLM Reasoning