mlsanityData to 14 Oct 2026

Advancing the Search Frontier with AI Agents

arXiv 2311.01235 ↗First posted 2 Nov 2023cs.IR cs.AIcited by 12

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Abstract (arXiv)

As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. There are profound implications from these advances for the design of intelligent systems and for the future of search itself. This article, based on a keynote by the author at the 2023 ACM SIGIR Conference, explores these issues and how AI agents are advancing the frontier of search system capabilities, with a special focus on information interaction and complex task completion.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 22 of its 80 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

22 of 80 references.

References · 22

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10 more
  1. 2022Faithful Reasoning Using Large Language Modelscited by 130
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 7.

12 papers.