arXiv AI
Jun 2
TAPS: Target-Aware Prefix Tree Selection for Diffusion-Drafted Speculative Decoding
arXiv:2606. 00487v1 Announce Type: new Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding.
PRESTO: Prefix-Aligned Tree Drafting for Diffusion Speculative Decoding
arXiv:2607. 22634v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, generating tokens in parallel.
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arXiv Machine Learning
Jun 4
D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models
arXiv:2606. 04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model forward pass.
arXiv Computation and Language
Aug 24
GRAFT: Adaptive DLM-Based Draft Tree Construction with Target-Distilled Edge Scoring
arXiv:2608.20375v1 Announce Type: new Abstract: Tree-based speculative decoding raises the mean accepted tokens of standard speculative decoding by verifying multiple draft paths, and existing tree b...
Hugging Face Trending Papers
Aug 13
DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees
Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel, but their position-wise distributions are marginal rather than conditioned on tokens selected along each draft path.
arXiv AI
Aug 11
LibraSpec: Dynamic Diffusion-Based Speculative Decoding via Marginal-Gain-Driven Optimization
arXiv:2608. 08721v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting multiple tokens for parallel verification, with efficiency critically determined by the speculative length selected at each decoding round.
arXiv Machine Learning
Aug 14
DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees
arXiv:2608. 13524v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel.
arXiv AI
Jun 12
Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs
arXiv:2509. 18085v4 Announce Type: replace-cross Abstract: Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token-generation rates.
Hugging Face Trending Papers
Jun 10
Teaching Diffusion to Speculate Left-to-Right
Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation. Speculative decoding addresses this bottleneck by employing a lightweight draft model to propose multiple future tokens that are subsequently verified in parallel by a larger target model.
arXiv Machine Learning
Jun 11
Teaching Diffusion to Speculate Left-to-Right
arXiv:2606. 11552v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation.
arXiv Machine Learning
Jul 22
AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
arXiv:2607. 19223v1 Announce Type: new Abstract: Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified in parallel by the target model, has become a prevalent paradigm for accelerating large language model inference.
Hugging Face Trending Papers
Jun 11
Accelerating Speculative Diffusions via Block Verification
Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme that ensures that the output matches the target distribution. Adapting this to continuous diffusions is difficult because speculative sampling requires drawing from a residual distribution.
arXiv Computation and Language
3d ago
DEdit: Iterative Draft Editing for Speculative Decoding
arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...