Problem
Research questions and friction points this paper is trying to address.
user motivations
behavior understanding
personal AI
unstructured interactions
user needs
Behavior Latticing: Inferring User Motivations from Unstructured Interactions
🤖 AI Summary
Current AI systems predominantly focus on representing user behaviors while overlooking the underlying motivations, thereby failing to address users’ deeper needs. This work proposes a novel “behavior latticing” architecture that, for the first time, models semantic relationships among discrete interaction behaviors using a lattice structure. By integrating multi-turn unstructured interaction data, the approach enables cross-behavioral inference of latent user motivations, uncovering implicit needs even users themselves may not recognize, and generating interpretable insights. Experimental results demonstrate that the proposed method significantly outperforms existing techniques in both motivation identification accuracy and depth of interpretability. The framework has been successfully deployed in personal AI agents, enhancing immediate utility while better aligning with long-term user value.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Behavior Latticing
user motivation inference
personal AI
unstructured interaction
behavior synthesis
🔎 Similar Papers
No similar papers found.
