arXiv AI
Jul 7
Training Hybrid Block Diffusion Language Models with Partial Bidirectionality
arXiv:2607. 02805v1 Announce Type: cross Abstract: High-throughput long-context generation is one of the central challenges for large language models.
Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale
The paper investigates how block‑diffusion language models can use a constant‑size cache to enable efficient parallel decoding. By employing sequence mixers that summarize completed blocks into a reusable state and a block‑causal training objective, the authors pretrain three 3B block‑diffusion denoisers (attention, Mamba, and hybrid) on 300 B tokens. The resulting state‑space cache remains O(1) in memory and latency regardless of context length, yielding significant speed‑up and memory savings compared to traditional attention‑based caches, especially at very long sequences.
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Sep 1
DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving
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arXiv Machine Learning
Aug 10
Retrofitting Linear Attention into Diffusion Language Models
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arXiv AI
Jul 21
FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models
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arXiv AI
Jun 29
Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation
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Hugging Face Trending Papers
Jul 5
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Hugging Face Trending Papers
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FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models
Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial. Prior work has attempted to unlock inter-block parallelism through post-training methods, but achieves only modest speedups and often degrades accuracy.
arXiv AI
Aug 13
LinearKV: One Cached State Suffices for Position-Independent Caching in Hybrid LLMs
arXiv:2608. 11231v1 Announce Type: new Abstract: LLM serving is increasingly accelerated by position-independent caching (PIC).
arXiv AI
Aug 21
Daedalus-150M: A Convolution-Attention Hybrid Designed for CPU Inference
arXiv:2608. 20210v1 Announce Type: cross Abstract: Small language models are usually built like large ones and then squeezed onto a CPU afterwards.
arXiv AI
Jun 3
dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching
arXiv:2506. 06295v2 Announce Type: replace-cross Abstract: Autoregressive Models (ARMs) have long dominated the landscape of Large Language Models.
arXiv AI
Sep 1
Tail-Replay: Escaping the Curse of Linear Attention in Prefix Caching for Hybrid LLMs
arXiv:2608.30310v1 Announce Type: cross Abstract: Hybrid large language models interleave full-attention layers with linear-attention layers to reduce the cost of long-context inference. This structu...
arXiv Machine Learning
Jul 7
Sangam: Efficiently Serving Diffusion LLMs with the AR Stack
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