Mind Mapper: Modeling and Predicting Behavioral Patterns from Everyday Conversations with Wearable AI Systems and LLMs — MIT Media Lab

Everyday conversations are more than exchanges of words—they reveal how people think, react, and adapt across situations. Through our speech we revea…

Danry, V*., Billa, J. G.*, Samaradivakara, Y.*, Liang, P. P., Maes, P. (2026, March). Mind Mapper: Modeling and Predicting Behavioral Patterns from Everyday Conversations with Wearable AI Systems and LLMs. In Proceedings of the 31th International Conference on Intelligent User Interfaces. Abstract Everyday conversations are more than exchanges of words—they reveal how people think, react, and adapt across situations. Through our speech we reveal the cognitive patterns that shape our behavior over time. Yet much of these patterns remains implicit: people operate through recurring heuristics…

saved by

• Haein Jung

related reading

• Interaction Models: A Scalable Approach to Human-AI Collaboration - Thinking Machines Labthinkingmachines.ai
• Verbalizable Representations Form a Global Workspace in Language Modelstransformer-circuits.pub
• [2304.03442] Generative Agents: Interactive Simulacra of Human Behaviorarxiv.org
• [2304.03442] Generative Agents: Interactive Simulacra of Human Behaviorarxiv.org
• [2603.05923] Learning Next Action Predictors from Human-Computer Interactionarxiv.org
• [2604.16812] Introspection Adapters: Training LLMs to Report Their Learned Behaviorsarxiv.org
• Co-Creating Life Goals from Everyday Computer Usearxiv.org
• [2505.10831] Creating General User Models from Computer Usearxiv.org
• Introspection Adapters: Training LLMs to Report Their Learned Behaviorsarxiv.org
• [2605.21739] AttuneBench: A Conversation-Based Benchmark for LLM Emotional Intelligencearxiv.org
• Introspection Adapters: Training LLMs to Report Their Learned Behaviorsarxiv.org
• Mechanistically Eliciting Latent Behaviors in Language Models — AI Alignment Forumalignmentforum.org