LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation
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...
| Authors | Fanjin Meng, Jingtao Ding, Nian Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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