Agentic Information Retrieval
*English edition of a zhichai.net forum entry (originally posted in Chinese).*
Overview
| Field | Content | |---|---| | Title | Agentic Information Retrieval | | Authors / Affiliation | Weinan Zhang, Junwei Liao, Ning Li, Kounianhua Du, Jianghao Lin | | Published | 2024-10-13 | | Source | https://arxiv.org/abs/2410.09713 | | Type | Academic paper (position/survey) |
Background and Motivation
Since the 1970s, information retrieval (IR) has been defined as acquiring relevant information items from a pre-defined corpus to satisfy user information needs. Traditional IR systems—while effective for web search—are constrained by their reliance on static, pre-defined information items, which poorly fits modern expectations of natural-language interaction, multi-hop reasoning, and up-to-date knowledge.
The paper introduces agentic information retrieval (Agentic IR), a transformative next-generation paradigm for IR driven by large language models (LLMs) and AI agents.
Core Idea
The central shift is the evolving definition of "information": from static, pre-defined information items to dynamic, context-dependent information states. An information state is the particular information context a user is in within a dynamic environment, including acquired information items, real-time user preferences, contextual factors, and decision-making processes. Traditional IR—retrieving items given a query—thus naturally extends to achieving a target information state given a user instruction, which defines Agentic IR.
Systematic Coverage
The paper discusses Agentic IR from several aspects:
- Task formulation and problem definition
- Architecture: retrievers, rerankers, planners, memory modules, tool interfaces, feedback mechanisms
- Evaluation: from static ranking metrics toward task success, citation accuracy, and process quality
- Case studies across open-domain search, conversational search, enterprise knowledge retrieval, and recommendation
- Challenges and future prospects: evaluation reliability, latency and cost, hallucination and safety, cross-lingual and multimodal extension
- Enterprise knowledge retrieval frameworks
- RAG surveys (vision and reasoning-oriented)
- Reinforcement-learning-based search agents (e.g., AceSearcher)
- Industrial agentic recommender systems
- Agentic Information Retrieval, arXiv:2410.09713. https://arxiv.org/abs/2410.09713
Original Abstract (preserved)
> Since the 1970s, information retrieval (IR) has long been defined as the process of acquiring relevant information items from a pre-defined corpus to satisfy user information needs. Traditional IR systems, while effective in domains like web search, are constrained by their reliance on static, pre-defined information items. To this end, this paper introduces agentic information retrieval (Agentic IR), a transformative next-generation paradigm for IR driven by large language models (LLMs) and AI agents. The central shift in agentic IR is the evolving definition of "information" from static, pre-defined information items to dynamic, context-dependent information states. [...] We systematically discuss agentic IR from various aspects, i.e., task formulation, architecture, evaluation, case studies, as well as challenges and future prospects. We believe that the concept of agentic IR introduced in this paper not only broadens the scope of information retrieval research but also lays the foundation for a more adaptive, interactive, and intelligent next-generation IR paradigm.
Key Takeaways for Search / Rec / Personalization
1. Architecture: cascade retrieval + reranking + generation remains mainstream, but agentic approaches make retrieval count and strategy themselves learnable. 2. Data: high-quality instruction data and click/session logs matter equally; synthetic data must guard against leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human assessment. 4. Deployment: latency, cost, explainability, and safety are hard industrial constraints—not just academic benchmarks.
Related Reading
Readers can navigate complementary literature along the chain: retrieval → ranking → generation/agent → evaluation.