LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation

2026-04-26

AI Summary

This work addresses key challenges in modeling human daily activity sequences—namely, long-tailed distributions, limited interpretability, and the difficulty of unifying diverse tasks—by proposing a Behavior Understanding Alignment (BUA) framework. BUA is the first approach to integrate large language models (LLMs) into behavior modeling through structured curriculum learning, leveraging sequence representations from pretrained behavior models as alignment anchors. By combining a three-stage curriculum with a multi-turn dialogue mechanism, the framework unifies behavior prediction and generation within a single architecture. BUA effectively bridges the semantic gap between behavioral data and natural language modalities, significantly outperforming existing methods on two real-world datasets while offering strong interpretability and robust multi-task generalization capabilities.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Human daily behavior unfolds as complex sequences shaped by intentions, preferences, and context. Effectively modeling these behaviors is crucial for intelligent systems such as personal assistants and recommendation engines. While recent advances in deep learning and behavior pre-training have improved behavior prediction, key challenges remain--particularly in handling long-tail behaviors, enhancing interpretability, and supporting multiple tasks within a unified framework. Large language models (LLMs) offer a promising direction due to their semantic richness, strong interpretability, and generative capabilities. However, the structural and modal differences between behavioral data and natural language limit the direct applicability of LLMs. To address this gap, we propose Behavior Understanding Alignment (BUA), a novel framework that integrates LLMs into human behavior modeling through a structured curriculum learning process. BUA employs sequence embeddings from pretrained behavior models as alignment anchors and guides the LLM through a three-stage curriculum, while a multi-round dialogue setting introduces prediction and generation capabilities. Experiments on two real-world datasets demonstrate that BUA significantly outperforms existing methods in both tasks, highlighting its effectiveness and flexibility in applying LLMs to complex human behavior modeling.

Problem

• human behavior modeling
• large language models
• behavior prediction
• long-tail behaviors
• multi-task learning

Innovation

• Behavior Understanding Alignment
• Large Language Models
• Curriculum Learning
• Behavior Prediction and Generation
• Sequence Embedding