Towards AI Search Paradigm

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 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.