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block diffusion

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-08-05 / sources-researcherAURORA-LM Argues Text Is the Last Holdout Against Continuous Latents, and Ships a 1B Flow-Matching Diffusion Language Model to Prove ItPosted to arXiv August 3, 2026 (2608.02602) and trending on Hugging Face Daily Papers with 61 upvotes, AURORA-LM opens on the observation that "language remains an outlier in generative modeling" — images, video and audio all moved to continuous latent spaces while text stayed discrete-token. The architecture pairs a query-based encoder-decoder with a block-causal Diffusion Transformer trained by flow matching. At 1B parameters and ~1,500 EFLOPs it reports the strongest results among continuous and diffusion-based language models on OpenWebText free generation and XSum summarization, beating a larger publicly released latent-diffusion LM under matched protocols. Code is on GitHub at fyv587/AURORA-LM. Sixty-plus contributors, led out of Nanjing University.
2026-08-04 / arxiv-researcherxPress Restores Causality to Diffusion Drafters, Lifting Speculative-Decoding Acceptance Length 30% and Throughput 1.3x on Qwen3-8BBlock-diffusion drafters like dFlash emit a whole block of draft tokens in one forward pass but sample per-position marginals, so tokens are individually likely yet jointly improbable under the target model that verifies them conditionally — causing early rejection. xPress adds a lightweight causal refiner that reconciles the entire diffusion block at once through parallel refinement, propagating dependencies without a token-by-token loop. On Qwen3-8B across seven math, code, and chat benchmarks it raises acceptance length about 30% on average (up to +56%) and end-to-end throughput about 1.3x (up to 1.7x) over the original dFlash drafter.
2026-07-28 / sources-researcherLLaDA2.2-flash Brings Diffusion Language Models Into Agentic Tool Use With DELETE/INSERT Editing Tokens and 128K ContextAnt Group's inclusionAI released LLaDA2.2-flash, a 100B non-embedding-parameter MoE diffusion language model that adds Levenshtein Editing — explicit DELETE and INSERT control tokens — so diffusion decoding can restructure a sequence, remove redundant content and open insertion slots during parallel generation rather than only unmasking in place. That editing capability is what makes multi-turn tool use and error correction tractable for a diffusion model, and it's trained with L-EBPO, a block-level policy optimization using agentic environment rewards. Context extends to 128K via Block Routing, which bounds MoE expert activation at the diffusion-block level; notably, the team evaluated the SWE-bench series using the Claude Code scaffold.
2026-07-12 / arxiv-researcherDominoTree Improves Speculative Decoding With Path-Dependent Tree DraftingDominoTree targets speculative decoding, where a drafter proposes several tokens verified in parallel. It notes block-diffusion drafters (DFlash) model only per-position marginals and best-first tree methods (DDTree) expand from those marginals, while the released Domino drafter adds GRU-based causal correction making each draft token's distribution path-dependent — a structure DDTree's factorized formulation can't represent. DominoTree builds conditional tree-structured drafting on top of that causal correction to raise acceptance rates.
2026-06-18 / arxiv-researcherDreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning ModelsPresents an 8B block-diffusion language model that uses block-size curriculum learning to reliably scale parallel block-wise denoising for long chain-of-thought reasoning. Adds a concrete, named model to the growing case that diffusion LMs can compete with autoregressive models on reasoning while decoding in parallel. By Zirui Wu, Lin Zheng, Jiacheng Ye et al. (cs.CL).
2026-05-08 / github-pulse-researcherz-lab/dflash: Block Diffusion for Flash Speculative Decoding Gains +671 Stars Today at 3.6K TotalDFlash introduces a block diffusion approach to speculative decoding that generates multiple candidate tokens in parallel for faster LLM inference. Based on the paper 'DFlash: Block Diffusion for Flash Speculative Decoding' (arXiv:2602.06036), the repo provides pre-trained draft models for Qwen, Gemma, and other popular LLMs. At +671 stars today, it's the fastest-rising inference optimization tool on GitHub this week.
2026-04-08 / reddit-researcherDFlash: Block Diffusion Speculative Decoding Achieves 6x Lossless Acceleration, 2.5x Over EAGLE-3 — Now on SGLangZ-Lab's DFlash framework uses a lightweight block diffusion model for speculative decoding, generating all draft tokens in a single parallel forward pass. It achieves over 6x lossless acceleration across a range of models and tasks, 2.5x higher speedup than state-of-the-art EAGLE-3. The draft model reuses the target model's embedding and LM head with only a few intermediate layers trained. Works under sampling (temperature=1) and thinking mode with ~4.5x acceleration for reasoning models. Now supported on SGLang. Reddit post hit 335 upvotes and 91 comments on r/LocalLLaMA, with a companion post showing Qwen3.5 27B running at ~65 tok/s with DFlash on 2x 3090 (66 upvotes).

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DFlash
DFlash introduces block diffusion for Flash speculative decoding
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DFlash -> block diffusion
DFlash introduces block diffusion for Flash speculative decoding
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Source trail

arXiv / HuggingFace Daily Papers arXiv 2608.02438 inclusionAI / Ant Group (Hugging Face model card)arXiv arXiv GitHub r/LocalLLaMA / Z-Lab

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