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ChronicleRec is a pre-train-and-transfer framework that compresses an ultra-long behavior sequence once into a chronologically ordered set of Chronicle Tokens, which can be cached per user, decoupling ultra-long sequence modeling from online candidate scoring.
DPGFlow is proposed, the first Flow Matching framework specifically designed for CDSR, which enables stable and efficient few-step inference and facilitates effective knowledge transfer under heterogeneous and noisy behaviors.
This paper empirically identifies two structural flaws rooted in geometric properties and dataset sparsity, and proposes a novel two-stage framework to close the long-short-view performance gap and introduces a modified softmax to counter prefix position bias.
MMEACR introduces a dual-track memory architecture that separates interpretable agent reasoning from fine-grained multimodal matching, and achieves strong overall performance against competitive LLM-based and agent-based baselines.
AUSO (Action-level Unified Skill Optimization), which unifies skill learning and skill use through a progressive, action-aware optimization process, and consistently improves agent performance and out-of-distribution generalization over competitive baselines.
This work proposes Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating external ICD coding guidelines as structured knowledge, and enables tight coupling between domain knowledge and LLM reasoning.
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