PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
This paper introduces PraMem, a method that constructs experiential memory by practicing on long historical sequences to address challenges in long-horizon behavior prediction with LLMs, achieving superior performance.
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[Submitted on 3 Jul 2026]
Title:PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
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Abstract: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: this https URL.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.02881 [cs.CL]
(or arXiv:2607.02881v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.02881
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhuoqun Li [view email] [v1] Fri, 3 Jul 2026 02:25:48 UTC (1,216 KB)
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