arXiv AI
Jun 16
Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving
arXiv:2512. 22420v5 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates LLM inference by verifying draft tokens in parallel.
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
arXiv:2607. 05147v1 Announce Type: new Abstract: Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification.
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arXiv AI
Jun 2
MineDraft: A Framework for Batch Parallel Speculative Decoding
arXiv:2603. 18016v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model.
arXiv AI
Jun 2
Hybrid Verified Decoding: Learning to Allocate Verification in Speculative Decoding
arXiv:2606. 01019v1 Announce Type: cross Abstract: Large Language Model (LLM) generation remains expensive because autoregressive decoding calls the model once for each new token.
arXiv Machine Learning
Jun 18
EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
arXiv:2606. 18967v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for LLMs, enabling strong reasoning and agentic capabilities.
arXiv Machine Learning
Jul 7
AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference
arXiv:2607. 03876v1 Announce Type: new Abstract: With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an approach capable of reliably delivering these models at scale even under severe memory bandwidth constraints such as those imposed by pure CPU implementations.
arXiv AI
Aug 12
MemSpec: Memory-Aware Runtime for Adaptive Draft Scheduling in Speculative Decoding on Edge Devices
arXiv:2608. 10362v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to speculate multiple tokens, reducing expensive target model decoding steps.
Hugging Face Trending Papers
Jul 27
DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference
Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, but their routed expert weights often exceed accelerator memory. We target latency-critical single-user settings where routed experts are staged on demand from CPU memory to a GPU or from Flash to a mobile NPU.
arXiv AI
Jun 11
VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
arXiv:2606. 12243v1 Announce Type: cross Abstract: Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel.
arXiv AI
Jul 28
DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference
arXiv:2607. 24434v1 Announce Type: cross Abstract: Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, but their routed expert weights often exceed accelerator memory.
Hugging Face Trending Papers
Jun 10
VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing draft-verify methods use binary decisions: accept or fully recompute.
arXiv AI
Jul 24
SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification
arXiv:2607. 20475v1 Announce Type: new Abstract: Sampling in LLM inference comprises a combinatorial set of logit processing, token selection, and verification operations for speculative decoding.
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.