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Advancing the Search Frontier with AI Agents

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arxiv 2311.01235 v2 pith:BCEUGZCM submitted 2023-11-02 cs.IR cs.AI

classification cs.IRcs.AI
keywords searchtaskcomplextasksagentsadvancingfrontierinformation
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Interaction as Intelligence: Deep Research With Human-AI Partnership

    cs.CL 2025-07 reject novelty 5.0 of 10

    A human-in-the-loop deep research system with transparent, interruptible interaction is claimed to outperform commercial baselines, but the evidence is weakened by small samples and biased instructions.

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