AI Models Can Predict and Collaboratively Modulate Human Memory Search

[2608.26152] AI Models Can Predict and Collaboratively Modulate Human Memory Search Skip to main content Search Submit Donate Log in Computer Science > Computation and Language arXiv:2608.26152 (cs) [Submitted on 3 Jul 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: AI Models Can Predict and Collaboratively Modulate Human Memory Search Authors: Eric Lacosse , Mariana Duarte , Graham Todd , Peter M. Todd , Daniel C. McNamee View PDF HTML (experimental) Abstract: Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example routine coding, these artificial intelligence models have reduced, or even eliminated, the need for human input. But rather than replacing human cognitive effort, LLMs may instead serve as cognitive tools to extend human abilities, particularly when they are engaged in a task requiring open-ended conceptual exploration and creative ideation. However, we are yet to understand how these models may enhance such generative human cognitive abilities in human--AI interactions. In this study, we explore and evaluate the ability of LLMs to follow and enhance human mental trajectories during semantic memory search. To test this, we use the semantic fluency task (SFT), a classic cognitive paradigm requiring generative semantic memory retrieval that has long served to characterize convergent and divergent thinking in humans. We demonstrate that an LLM's abilities to track and predict human memory trajectories in this task exceed those of other humans. Comments: 18 pages, 5 figures; includes Supplementary Information Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.26152 [cs.CL] (or arXiv:2608.26152v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.26152 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Eric Lacosse [ view email ] [v1] Fri, 3 Jul 2026 05:15:14 UTC (10,438 KB) [v2] Fri, 28 Aug 2026 09:42:54 UTC (8,450 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.HC References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

Related documents

• AI Models Can Predict and Collaboratively Modulate Human Memory Search#779369
• CWM: Controllable White-Box Meta-Prompting for Adaptive Retrieval-Augmented Generation and Reasoning Ability#1109276
• Heavy-Tailed Memory Traces in Long-Horizon Language Agents#1154633
• DeferMem: Query-Time Evidence Distillation via Reinforcement Learning for Long-Term Agent Memory#1117431
• Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents#821767