A Multi-Agent Perspective on Modern Information Retrieval
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
The rise of large language models (LLMs) has introduced a new era in information retrieval (IR), where queries and documents that were once assumed to be generated exclusively by humans can now also be created by automated agents. These agents can formulate queries, generate documents, and perform ranking. This shift challenges some long-standing IR paradigms and calls for a reassessment of both theoretical frameworks and practical methodologies. We advocate for a multi-agent perspective to better capture the complex interactions between query agents, document agents, and ranker agents. Through empirical exploration of various multi-agent retrieval settings, we reveal the significant impact of these interactions on system performance. Our findings underscore the need to revisit classical IR paradigms and develop new frameworks for more effective modeling and evaluation of modern retrieval systems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 22 of its 78 references that are in the map. First posted on arXiv 20 Feb 2025.
Builds on
22 of 78 references.
Closest ancestors · 2
Same direction, newest first
- 2025CSP: A Simulator For Multi-Agent Ranking Competitions16 Feb
- 2024Agentic Information Retrieval13 Oct
Other references · 20
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
- 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
- 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
- 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
- 2019Passage Re-ranking with BERTcited by 629
- 2023Large Language Models for Information Retrieval: A Surveycited by 331
- 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 302
- 2019Document Expansion by Query Predictioncited by 225
- 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
- 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 160
8 more
- 2022Holistic Evaluation of Language Modelscited by 115
- 2023Query Expansion by Prompting Large Language Modelscited by 56
- 2022PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Modelscited by 44
- 2024Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Mediacited by 35
- 2024Ranking Manipulation for Conversational Search Enginescited by 23
- 2021Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journalscited by 17
- 2024Generating Multi-Aspect Queries for Conversational Searchcited by 7
- 2025White Hat Search Engine Optimization using Large Language Modelscited by 4
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Citing papers per year: 2025: 2.
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