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TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems

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Optimizing communication topology in LLM-based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://anonymous.4open.science/r/TopoDIM-8D35/

Rui Sun, Jie Ding, Chenghua Gong, Tianjun Gu, Yihang Jiang, Juyuan Zhang, Liming Pan, Linyuan L\"u• 2026

Related benchmarks

TaskDatasetResultRank
Code GenerationHumanEval
Pass@195.83
850
Mathematical ReasoningAIME
AIME Accuracy80.34
283
Multiple-choice Question AnsweringMMLU-Pro
MMLU-Pro Overall Accuracy84.8
116
Mathematical ReasoningMultiArith
Accuracy100
116
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