Communication Topology Optimization

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11 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

An Information-Geometric Framework for OCS-Reconfigurable Interconnect Topology in Large-Scale AI Clusters
Oct 18, 2026Shintaro YoshizawaCommunication Topology Optimization Dynamic Regret Minimization
Nagoya Mathematical and Information Science Research
MedPrune: Topology-Efficient Multimodal Multi-Agent Communication Evolution for Medical VQA Tasks
Oct 5, 2026Jiuheng Wan, Runze Li, Chen Chen +5Communication Topology Optimization Medical VQA
School of Computer Science and Information Engineering, Hefei University of Technology · Nanjing University · Guangdong University of Finance and Economics +2
Codebook Agent: Amortized Topology Design for LLM Multi-Agent Systems
Sep 2, 2026Jinxi Yu, Yubei Li, Eric Hanchen Jiang +6Communication Topology Optimization Multi-Agent LLM Systems
University of California, Los Angeles
Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning
Jul 5, 2026Kaushal Attaluri, Rebeca P. Diaz-Redondo, Manuel Fernandez VeigaCommunication Topology Optimization Federated Learning Aggregation
ICLab, atlanTTic, University of Vigo
TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
May 27, 2026Yi Ding, Zijie Xuan, Haowei Zhou +6Evolutionary Optimization Multi-Agent System Optimization
National Institute of Metrology, China · University of California, Berkeley · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences +5
Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems
May 7, 2026Huchen Yang, Xinghao Dong, Dan Negrut +1Multi-Agent LLM Systems Multi-Agent System Optimization
University of Wisconsin–Madison
Cayley Graph Optimization for Scalable Multi-Agent Communication Topologies
Apr 7, 2026Jingkai Luo, Yulin ShaoCommunication Topology Optimization Multi-Agent Communication Topology
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization
Mar 3, 2026Yueyang Cang, Xiaoteng Zhang, Erlu Zhao +7Communication Topology Optimization Multi-Agent Communication Topology
Tsinghua University · Donghua University
RUMAD: Reinforcement-Unifying Multi-Agent Debate
Feb 27, 2026Chao Wang, Han Lin, Huaze Tang +2Communication Topology Optimization Multi-Agent Debate
Tsinghua University, Shenzhen, China · Zhejiang University, Hangzhou, China
HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems
Feb 23, 2026Tianjun Yao, Zhaoyi Li, Zhiqiang ShenCommunication Topology Optimization Multi-Agent LLM Systems
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
Self-Configurable Mesh-Networks for Scalable Distributed Submodular Bandit Optimization
Feb 22, 2026Zirui Xu, Vasileios TzoumasMulti-Agent Coordination Submodular Optimization
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 USA

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An Information-Geometric Framework for OCS-Reconfigurable Interconnect Topology in Large-Scale AI Clusters

Shintaro Yoshizawa

Nagoya Mathematical and Information Science Research

Communication Topology Optimization Dynamic Regret Minimization

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Optical Circuit Switches (OCS) make inter-pod wiring in AI clusters programmable, posing design questions on wiring choice, symmetry departure, and reconfigurations. We present a unified information-geometric framework to address these issues.A wiring is a point in a probability simplex over circuits within a degree polytope. Departure from a symmetric Fat-Tree prior

\mathbf{q}

is measured by

\DKL(\cdot \,\|\, \mathbf{q})

. The cost

U

is the smoothed DAG critical-path length, whose gradient equals path marginals of a Gibbs distribution via forward--backward recursion.For free energy

F_{β_1} = U + β_1^{-1} \DKL(\cdot \,\|\, \mathbf{q})

, we prove well-posedness, monotonic dependence on

β_1

, adaptation bounds via Jeffreys divergence, and a symmetry principle for static optimality. An entropic proximal-gradient method updates multiplicatively with a

|V|

-dim dual KL projection, achieving linear convergence.For time-varying traffic, a threshold policy yields regret

O(\sqrt{ΓC V_1 T})

against the switching-free optimum. Traffic changes are detected by a multinomial GLR--CUSUM detector with non-asymptotic false-alarm bounds. Multi-job generalized-mean fairness aligns with

α

-fairness (

α\ge 2

). Experiments on pipeline, data, and MoE workloads validate the framework against integer programming.