arXiv Computation and Language August 31, 2026 By Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee

AI Models Can Predict and Collaboratively Modulate Human Memory Search

Read the original on arXiv Computation and Language →

The study investigates how large language models (LLMs) can assist humans in semantic memory search tasks. By using the semantic fluency task (SFT), the researchers evaluate whether LLMs can follow and enhance human mental trajectories during generative semantic retrieval. Results show that an LLM’s ability to track and predict human memory trajectories in this task surpasses that of other humans.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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