Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong, Xinran Chen, Jiamin Chen, Jun Yang, Haojie Zhang, Jiayi Li, Jiayi Wu, Yiqun Chen, Changle Qu, Wenwen Ye, Lixin Su, Xinyu Ma, Lingyong Yan, Long Xia, Daiting Shi, Junfeng Wang, Xiangyu Zhao, Jiashu Zhao, Haoyi Xiong, Shuaiqiang Wang, Dawei Yin

TL;DR
This paper proposes the AI Search Paradigm, a modular, multi-agent framework using large language models to create adaptable, human-like search systems capable of handling simple to complex information tasks.
Contribution
It introduces a novel multi-agent architecture with coordinated workflows for dynamic problem decomposition, tool integration, and content synthesis in AI search systems.
Findings
Demonstrates effective task planning and tool use coordination.
Provides methodologies for robust retrieval-augmented generation.
Offers infrastructure strategies for scalable LLM inference.
Abstract
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…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Semantic Web and Ontologies · AI-based Problem Solving and Planning
