LogitSpec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation (2026.findings-acl)
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| Challenge: | Speculative decoding (SD) is a promising technique for LLM inference acceleration. |
| Approach: | They propose a method to generate draft tokens in a retrieval-based manner to reduce drafting overhead and improve inference speed. |
| Outcome: | Extensive tests show that *LogitSpec* can achieve 2.61 speedup and 3.28 mean accepted tokens per decoding step. |
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| Challenge: | Speculative decoding (SD) uses an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs. |
| Approach: | They propose a draft model that proposes the next few tokens from a non-parametric datastore and uses a framework that uses approximate nearest neighbour search with contextualised token embeddings to retrieve the most semantically relevant sequences for SD. |
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Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism (2026.acl-long)
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| Challenge: | Parallel Speculative Decoding (PSD) has limitations due to speedup limits and high computational waste . a novel synchronous mechanism solves the Retrieval Precision-Efficiency Dilemma . |
| Approach: | They propose a framework that combines a draft-verification-based approach with a synchronous mechanism to solve the Retrieval Precision-Efficiency Dilemma. |
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RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding (2026.findings-acl)
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| Challenge: | Existing methods for decoding large language models generate one token per step, causing high inference latency. |
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& Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)
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| Challenge: | Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding. |
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Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Antonie Lin, Mohammad Rastegari, Mahyar Najibi
| Challenge: | Speculative decoding is a prominent technique for accelerating LLM inference by leveraging an auxiliary draft model, but its effectiveness is limited by the autoregressive nature of draft generation. |
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UniSpec: Training-Free Speculative Decoding for Robust LLM Acceleration Across Languages and Hardware (2026.acl-long)
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| Challenge: | Existing methods for speculative decoding ignore device-specific verification costs and lack of mechanisms to assess draft token quality. |
| Approach: | They propose a training-free, lossless speculative decoding framework that enables robust, plug-and-play LLM acceleration across diverse hardware configurations and languages. |
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RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
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| Challenge: | Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases. |
| Approach: | They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree. |
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SpecHub: Provable Acceleration to Multi-Draft Speculative Decoding (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have limited inference speed due to sequential token generation . Spechub is a novel, efficient sampling-verification method for MDSD that improves acceptance rates with only linear computational overhead. |
| Approach: | They propose a method that uses a smaller draft model to generate multiple token sequences . Spechub generates 0.05-0.27 and 0.02-0.16 more tokens per step than RRS and RRS without replacement . |
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Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism (2024.findings-acl)
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| Challenge: | Existing approaches to generate draft tokens in large language models are expensive and resource-intensive. |
| Approach: | They propose an approach to generate draft tokens using a segment of the LLM and a self-distillation method to enhance the quality of draft token. |
| Outcome: | The proposed approach generates draft tokens using a segment of the LLM and a self-distillation method to improve quality and speed up generation. |
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation. |
| Approach: | They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model. |
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