AI Models Can Predict and Collaboratively Modulate Human Memory Search

arXiv:2608.26152 · cs.CL, cs.AI, cs.HC · Submitted 2026-07-03 · Read on arXiv

cs.CL, cs.AI, cs.HC

Submitted: 2026-07-03

Updated: 2026-08-28

Code: https://github.com/nslothai/unsloth

Terminology

• Alignment — 63× in this paper · explained in 1 episode
• Accuracy — 29× in this paper · explained in 1 episode
• Semantic Trajectories — 15× in this paper · explained in 2 episodes
• Cosine Similarity — 14× in this paper · explained in 3 episodes
• Perplexity — 14× in this paper · explained in 3 episodes
• Core — 13× in this paper · explained in 2 episodes
• Commentary — 12× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• In-Context Learning — 7× in this paper · explained in 4 episodes
• Spectral Gap — 7× in this paper · explained in 5 episodes
• Jaccard Index — 6× in this paper
• Clustering — 5× in this paper · explained in 2 episodes
• AI Alignment — 4× in this paper · explained in 2 episodes
• Transition Probability Matrix — 4× in this paper
• Generative AI — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Alternation — 2× in this paper · explained in 1 episode
• Scaffolding — 2× in this paper · explained in 3 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Theory-Driven Cognitive Prompting — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Cognitive Scaffolding — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• First-Order Transition — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Theory-Driven Cognitive Prompt — 1× in this paper
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Measuring and mitigating overreliance to build human-compatible AI
• Beyond Preferences in AI Alignment
• A Mental-Model Centric Landscape of Human-AI Symbiosis
• Charting trajectories of human thought using large language models
• Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
• Concept Alignment
• Towards a Path Dependent Account of Category Fluency
• Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task
• Unlocking Structured Thinking in Language Models with Cognitive Prompting
• Language models are better than humans at next-token prediction
• Where is the Mind? Persona Vectors and LLM Individuation
• Reasoning Language Models: A Blueprint
• From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
• The Llama 3 Herd of Models
• Signatures of human-like processing in Transformer forward passes
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Mixtral of Experts
• FastText.zip: Compressing text classification models

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