# Advancing the Search Frontier with AI Agents arXiv: https://arxiv.org/abs/2311.01235 ยท HTML: https://mlsanity.com/paper/2311.01235/ ## 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 (most cited first, 22 of 22) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019) - [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023) - [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023) - [Differentially Private Fine-tuning of Language Models](https://mlsanity.com/paper/2110.06500/) (2021) - [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://mlsanity.com/paper/2306.02707/) (2023) - [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021) - [Large language models can accurately predict searcher preferences](https://mlsanity.com/paper/2309.10621/) (2023) - [Faithful Reasoning Using Large Language Models](https://mlsanity.com/paper/2208.14271/) (2022) - [Perspectives on Large Language Models for Relevance Judgment](https://mlsanity.com/paper/2304.09161/) (2023) - [EcoAssistant: Using LLM Assistant More Affordably and Accurately](https://mlsanity.com/paper/2310.03046/) (2023) - [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022) - [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022) - [Conversations with Documents. An Exploration of Document-Centered Assistance](https://mlsanity.com/paper/2002.00747/) (2020) - [Synthetic Cross-language Information Retrieval Training Data](https://mlsanity.com/paper/2305.00331/) (2023) - [How does AI chat change search behaviors?](https://mlsanity.com/paper/2307.03826/) (2023) - [Taking Search to Task](https://mlsanity.com/paper/2301.05046/) (2023) - [Delphic Costs and Benefits in Web Search: A utilitarian and historical analysis](https://mlsanity.com/paper/2308.07525/) (2023) ## Built on by (most cited first, 12 of 12) - [DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports](https://mlsanity.com/paper/2601.08536/) (2026) - [AgentIR: Reasoning-Aware Retrieval for Deep Research Agents](https://mlsanity.com/paper/2603.04384/) (2026) - [Interaction as Intelligence: Deep Research With Human-AI Partnership](https://mlsanity.com/paper/2507.15759/) (2025) - [NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search](https://mlsanity.com/paper/2505.14680/) (2025) - [Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit](https://mlsanity.com/paper/2510.07226/) (2025) - [Learning to Retrieve from Agent Trajectories](https://mlsanity.com/paper/2604.04949/) (2026) - [Panmodal Information Interaction](https://mlsanity.com/paper/2405.12923/) (2024) - [ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents](https://mlsanity.com/paper/2604.25849/) (2026) - [ITER: Interaction-Aware Retrieval for Agentic Search](https://mlsanity.com/paper/2608.27912/) (2026) - [Automated Meta Prompt Engineering for Alignment with the Theory of Mind](https://mlsanity.com/paper/2505.09024/) (2025) - [From SERPs to Sound: How Search Engine Result Pages and AI-generated Podcasts Interact to Influence User Attitudes on Controversial Topics](https://mlsanity.com/paper/2601.11282/) (2026) - [Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions](https://mlsanity.com/paper/2608.18110/) (2026) ## 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.