BlossomRec is a sparse attention mechanism that uses two distinct block-level patterns for long-term and short-term interests, fused by a gated output, to reduce computation in sequential recommendation Transformers.
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PDR is a user-context-aware framework for LLM research agents that improves report relevance over static baselines, supported by a new dataset and hybrid evaluation.
Tandem lets a large model supply compact strategic guidance to a small model for reasoning tasks, achieving similar or better performance at about 40 percent lower cost through adaptive early stopping.
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BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
BlossomRec is a sparse attention mechanism that uses two distinct block-level patterns for long-term and short-term interests, fused by a gated output, to reduce computation in sequential recommendation Transformers.
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Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery
PDR is a user-context-aware framework for LLM research agents that improves report relevance over static baselines, supported by a new dataset and hybrid evaluation.
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Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Tandem lets a large model supply compact strategic guidance to a small model for reasoning tasks, achieving similar or better performance at about 40 percent lower cost through adaptive early stopping.