arXiv AI
Tail-Replay: Escaping the Curse of Linear Attention in Prefix Caching for Hybrid LLMs
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DeltaLog: Deferred Materialization of Recurrent States for Linear Attention Decoding
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Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing
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DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling
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DART: Decoded Attention over Recurrent States for Efficient Long-Context Sequence Modeling
Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures. Transformers rely on token-level attention memories, while recurrent models such as state space models (SSMs) and linear attention maintain compact recurrent states.
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Training Hybrid Block Diffusion Language Models with Partial Bidirectionality
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