# AI Models Can Predict and Collaboratively Modulate Human Memory Search

arXiv: https://arxiv.org/abs/2608.26152 · HTML: https://mlsanity.com/paper/2608.26152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 93 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 19 of 19)

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- [Where is the Mind? Persona Vectors and LLM Individuation](https://mlsanity.com/paper/2604.17031/) (2026)
- [Unlocking Structured Thinking in Language Models with Cognitive Prompting](https://mlsanity.com/paper/2410.02953/) (2024)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)
- [Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task](https://mlsanity.com/paper/2505.16164/) (2025)
- [Signatures of human-like processing in Transformer forward passes](https://mlsanity.com/paper/2504.14107/) (2025)
- [Charting trajectories of human thought using large language models](https://mlsanity.com/paper/2509.14455/) (2025)
- [Towards a Path Dependent Account of Category Fluency](https://mlsanity.com/paper/2405.06714/) (2024)
- [Emerging Human-like Strategies for Semantic Memory Foraging in Large Language Models](https://mlsanity.com/paper/2603.01822/) (2026)

## Abstract (arXiv)

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.