Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918428711845888 |
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| author | Wu, Feijie Li, Zitao Wei, Fei Li, Yaliang Ding, Bolin Gao, Jing |
| author_facet | Wu, Feijie Li, Zitao Wei, Fei Li, Yaliang Ding, Bolin Gao, Jing |
| contents | Retrieval-augmented generation (RAG) agents are increasingly deployed to answer questions over local knowledge bases that cannot be centralized due to knowledge-sovereignty constraints. This results in two recurring failures in production: users do not know which agent to consult, and complex questions require evidence distributed across multiple agents. To overcome these challenges, we propose RIRS, a training-free orchestration framework to enable a multi-agent system for question answering. In detail, RIRS summarizes each agent's local corpus in an embedding space, enabling a user-facing server to route queries only to the most relevant agents, reducing latency and avoiding noisy "broadcast-to-all" contexts. For complicated questions, the server can iteratively aggregate responses to derive intermediate results and refine the question to bridge the gap toward a comprehensive answer. Extensive experiments demonstrate the effectiveness of RIRS, including its ability to precisely select agents and provide accurate responses to single-hop queries, and its use of an iterative strategy to achieve accurate, multi-step resolutions for complex queries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering Wu, Feijie Li, Zitao Wei, Fei Li, Yaliang Ding, Bolin Gao, Jing Multiagent Systems Artificial Intelligence Computation and Language Retrieval-augmented generation (RAG) agents are increasingly deployed to answer questions over local knowledge bases that cannot be centralized due to knowledge-sovereignty constraints. This results in two recurring failures in production: users do not know which agent to consult, and complex questions require evidence distributed across multiple agents. To overcome these challenges, we propose RIRS, a training-free orchestration framework to enable a multi-agent system for question answering. In detail, RIRS summarizes each agent's local corpus in an embedding space, enabling a user-facing server to route queries only to the most relevant agents, reducing latency and avoiding noisy "broadcast-to-all" contexts. For complicated questions, the server can iteratively aggregate responses to derive intermediate results and refine the question to bridge the gap toward a comprehensive answer. Extensive experiments demonstrate the effectiveness of RIRS, including its ability to precisely select agents and provide accurate responses to single-hop queries, and its use of an iterative strategy to achieve accurate, multi-step resolutions for complex queries. |
| title | Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering |
| topic | Multiagent Systems Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2501.07813 |