通过提前演练历史行为构建经验记忆，提升长期行为预测准确率
PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
- 将长序列历史行为转化为可利用资源，预先构建经验记忆
- 在多任务实验中表现优于现有方法，显著提升预测精度
- 适合需要长期行为建模的智能系统，如个性化推荐与人机交互
长时序行为预测旨在基于长时间的历史序列推断用户的下一步行为，在人工智能领域具有重要意义。大语言模型（LLMs）为序列行为预测提供了新方向，但在处理长时序预测时存在潜在行为模式识别困难及模型内在认知偏见问题。现有记忆管理方法遵循上下文压缩范式，试图通过减轻历史序列负担来应对，但未能解决核心挑战。本文提出范式转变：将冗长的历史序列从负担转化为可挖掘的宝贵资源，据此提出PraMem方法，通过对历史序列进行预先演练以构建经验记忆，作为辅助输入支持更精准的长时序行为预测。大量实验表明，PraMem在多个任务上均优于现有方法；深入分析揭示了经验记忆的机制与演化过程。代码已开源：https://github.com/icip-cas/PraMem。
原文摘要 · Abstract (English)
Long-horizon behavior prediction aims to infer a user's next action based on a lengthy historical sequence, playing a crucial role in artificial intelligence field. The rise of large language models (LLMs) offers a promising direction for sequential behavior prediction, yet LLMs 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 the 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 beforehand practice over the lengthy historical sequence to build an experiential memory, thereby serving as the assisted input for accurate long-horizon behavior prediction. Extensive experiments across diverse tasks demonstrate that PraMem achieves superior performance than prior methods, and more in-depth analyses provide valuable insights into the mechanism and evolution of the experiential memory. Code: https://github.com/icip-cas/PraMem.
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