Towards AI Search Paradigm
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908882489573376 |
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| author | Li, Yuchen Cai, Hengyi Kong, Rui Chen, Xinran Chen, Jiamin Yang, Jun Zhang, Haojie Li, Jiayi Wu, Jiayi Chen, Yiqun Qu, Changle Ye, Wenwen Su, Lixin Ma, Xinyu Yan, Lingyong Xia, Long Shi, Daiting Wang, Junfeng Zhao, Xiangyu Zhao, Jiashu Xiong, Haoyi Wang, Shuaiqiang Yin, Dawei |
| author_facet | Li, Yuchen Cai, Hengyi Kong, Rui Chen, Xinran Chen, Jiamin Yang, Jun Zhang, Haojie Li, Jiayi Wu, Jiayi Chen, Yiqun Qu, Changle Ye, Wenwen Su, Lixin Ma, Xinyu Yan, Lingyong Xia, Long Shi, Daiting Wang, Junfeng Zhao, Xiangyu Zhao, Jiashu Xiong, Haoyi Wang, Shuaiqiang Yin, Dawei |
| contents | In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to evaluate query complexity, decompose problems into executable plans, and orchestrate tool usage, task execution, and content synthesis. We systematically present key methodologies for realizing this paradigm, including task planning and tool integration, execution strategies, aligned and robust retrieval-augmented generation, and efficient LLM inference, spanning both algorithmic techniques and infrastructure-level optimizations. By providing an in-depth guide to these foundational components, this work aims to inform the development of trustworthy, adaptive, and scalable AI search systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards AI Search Paradigm Li, Yuchen Cai, Hengyi Kong, Rui Chen, Xinran Chen, Jiamin Yang, Jun Zhang, Haojie Li, Jiayi Wu, Jiayi Chen, Yiqun Qu, Changle Ye, Wenwen Su, Lixin Ma, Xinyu Yan, Lingyong Xia, Long Shi, Daiting Wang, Junfeng Zhao, Xiangyu Zhao, Jiashu Xiong, Haoyi Wang, Shuaiqiang Yin, Dawei Computation and Language Artificial Intelligence Information Retrieval In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to evaluate query complexity, decompose problems into executable plans, and orchestrate tool usage, task execution, and content synthesis. We systematically present key methodologies for realizing this paradigm, including task planning and tool integration, execution strategies, aligned and robust retrieval-augmented generation, and efficient LLM inference, spanning both algorithmic techniques and infrastructure-level optimizations. By providing an in-depth guide to these foundational components, this work aims to inform the development of trustworthy, adaptive, and scalable AI search systems. |
| title | Towards AI Search Paradigm |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2506.17188 |