RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913178679508992 |
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| author | Zhang, Zhen Zhou, Wanjing Li, Juncheng Fei, Hao Wen, Jun Ji, Wei |
| author_facet | Zhang, Zhen Zhou, Wanjing Li, Juncheng Fei, Hao Wen, Jun Ji, Wei |
| contents | Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, including code generation, mathematical reasoning, and planning, etc. Despite their impressive performance, the effectiveness and robustness of these systems heavily rely on their communication topology, which is often fixed or generated in a single step. This restricts fine-grained structural exploration and flexible composition, resulting in excessive token utilization on simple tasks while limiting capability on complicated tasks. To mitigate this challenge, we introduce RADAR, a redundancy-aware and query-adaptive generative framework that actively reduce communication overhead. Motivated by recent progress in conditional discrete graph diffusion models, we formulate communication topology design as a step-by-step generation process, guided by the effective size of the graph. Comprehensive experiments on six benchmarks demonstrate that RADAR consistently outperforms recent baselines, achieving higher accuracy, lower token consumption, and greater robustness across diverse scenarios. Our code and data are available at https://github.com/cszhangzhen/RADAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09907 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation Zhang, Zhen Zhou, Wanjing Li, Juncheng Fei, Hao Wen, Jun Ji, Wei Artificial Intelligence Multiagent Systems Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, including code generation, mathematical reasoning, and planning, etc. Despite their impressive performance, the effectiveness and robustness of these systems heavily rely on their communication topology, which is often fixed or generated in a single step. This restricts fine-grained structural exploration and flexible composition, resulting in excessive token utilization on simple tasks while limiting capability on complicated tasks. To mitigate this challenge, we introduce RADAR, a redundancy-aware and query-adaptive generative framework that actively reduce communication overhead. Motivated by recent progress in conditional discrete graph diffusion models, we formulate communication topology design as a step-by-step generation process, guided by the effective size of the graph. Comprehensive experiments on six benchmarks demonstrate that RADAR consistently outperforms recent baselines, achieving higher accuracy, lower token consumption, and greater robustness across diverse scenarios. Our code and data are available at https://github.com/cszhangzhen/RADAR. |
| title | RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2605.09907 |