PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction

Long-horizon behavior prediction plays a crucial role in artificial intelligence application, which aims to infer a user's next action based on a lengthy historical sequence. The rise of large language models (LLMs) offers a promising direction for sequential behavior prediction, but LLMs still struggle with latent behavioral pattern induction and model-intrinsic cognitive biases when tackling long-horizon behavior prediction. Prior memory management methods follow a context-compression paradigm that attempts to address this task by alleviating the historical sequence burden, yet fail to resolve core challenges. In this paper, we advocate a paradigm shift that reframes the lengthy historical sequence from a burden into a valuable resource to be exploited, and accordingly propose PraMem, which conducts practice over lengthy historical sequence to build an experiential memory, thereby serving as assisted input for accurate long-horizon behavior prediction. Extensive experiments across diverse tasks demonstrate PraMem achieves superior performance than prior methods, and more in-depth analyses provide valuable insights into mechanism of the experiential memory.