[2604.07629] Behavior Latticing: Inferring User Motivations from Unstructured Interactions

Abstract:A long-standing vision of computing is the personal AI system: one that understands us well enough to address our underlying needs. Today's AI focuses on what users do, ignoring why they might be doing such things in the first place. As a result, AI systems default to optimizing or repeating existing behaviors (e.g., user has ChatGPT complete their homework) even when they run counter to users' needs (e.g., gaining subject expertise). Instead we require systems that can make connections across observations, synthesizing them into insights about the motivations underlying these behaviors (e.g., user's ongoing commitments make it difficult to prioritize learning despite expressed desire to do so). We introduce an architecture for building user understanding through behavior latticing, connecting seemingly disparate behaviors, synthesizing them into insights, and repeating this process over long spans of interaction data. Doing so affords new capabilities, including being able to infer users' needs rather than just their tasks and connecting subtle patterns to produce conclusions that users themselves may not have previously realized. In an evaluation, we validate that behavior latticing produces accurate insights about the user with significantly greater interpretive depth compared to state-of-the-art approaches. To demonstrate the new interactive capabilities that behavior lattices afford, we instantiate a personal AI agent steered by user insights, finding that our agent is significantly better at addressing users' needs while still providing immediate utility.

View PDF HTML (experimental) Abstract:A long-standing vision of computing is the personal AI system: one that understands us well enough to address our underlying needs. Today's AI focuses on what users do, ignoring why they might be doing such things in the first place. As a result, AI systems default to optimizing or repeating existing behaviors (e.g., user has ChatGPT complete their homework) even when they run counter to users' needs (e.g., gaining subject expertise). Instead we require systems that can make connections across observations, synthesizing them into insights about the…

saved by

• Laerdon Kim

related reading

• LLMs Get Lost in Evolving User Intentarxiv.org
• Group | Sherry Tongshuang Wucs.cmu.edu
• Simulating Users with State Alignment Beats Response Imitationhumanlm.stanford.edu
• user_interactions.pdfself-distillation.github.io
• Co-Creating Life Goals from Everyday Computer Usearxiv.org
• Mind Mapper: Modeling and Predicting Behavioral Patterns from Everyday Conversations with Wearable AI Systems and LLMs – MIT Media Labmedia.mit.edu
• [2505.10831] Creating General User Models from Computer Usearxiv.org
• [2603.05923] Learning Next Action Predictors from Human-Computer Interactionarxiv.org
• Guardian Angels: LLM Personalization for Productivity and Security · Gwern.netgwern.net
• [2304.03442] Generative Agents: Interactive Simulacra of Human Behaviorarxiv.org
• From personas to intentions: towards a science of motivations for AI models — LessWronglesswrong.com
• The behavioral selection model for predicting AI motivations — LessWronglesswrong.com