arXiv AI August 28, 2026 By Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee

Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search

The article reports that large language models can predict and collaboratively modulate human memory search during a semantic fluency task. By tracking and forecasting participants’ semantic retrieval patterns, the models outperform other humans in following these mental trajectories. This suggests that AI can serve as a cognitive tool to extend human abilities in open‑ended conceptual exploration and creative ideation.

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 AI.

arXiv Computation and Language

Aug 31

AI Models Can Predict and Collaboratively Modulate Human Memory Search

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.

By Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee

llms fine-tuning

arXiv AI

Jul 15

Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing

arXiv:2607. 12195v1 Announce Type: cross Abstract: Semantic memory retrieval can be conceptualized as navigation through conceptual space.

By Gabriel Paris-Colombo, Rodrigo M. Cabral-Carvalho, Felipe D. Toro-Hern\'andez

llms rag nlp

arXiv AI

Jun 9

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

arXiv:2605. 12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context.

By Jiazhou Liang, Armin Toroghi, Yifan Simon Liu, Faeze Moradi Kalarde, Liam Gallagher, Scott Sanner

llms rag agents

arXiv AI

Jun 8

AdMem: Advanced Memory for Task-solving Agents

arXiv:2606. 06787v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.

By Runzhe Wang, Huilin Lu, Shengjie Liu, Li Dong, Jason Zhu

llms agents efficiency

Hugging Face Trending Papers

Jun 29

Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interaction, yet current agents lack the necessary persistent procedural memory.

llms rag agents benchmarks

arXiv AI

Jun 30

Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

arXiv:2606. 29824v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging.

By Chengfeng Zhao, Yuqiao Tan, Shizhu He, Yequan Wang, Jun Zhao, Kang Liu

llms rag agents benchmarks

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AI Models Can Predict and Collaboratively Modulate Human Memory Search

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