#LLM Inference

2026

• 07-01 EN

  SSV: Sparse Speculative Verification for Efficient LLM Inference

• 07-01 中

  SSV：稀疏投机验证——在动态稀疏注意力中做投机解码

• 06-29 EN

  ACTS: Steering How LLMs Reason, Not Just How Long

• 06-29 中

  ACTS：用强化学习训练的控制器，让 LLM 推理更聪明而不只是更短

• 06-28 EN

  Moebius: Seamless Runtime Parallelism Switching for MoE LLM Serving

• 06-28 中

  Moebius：为 MoE 大模型推理服务实现无缝运行时并行策略切换

• 06-27 EN

  JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting

• 06-27 中

  JetSpec：用并行树草稿突破推测解码的扩展上限

• 06-26 EN

  SigmaScale: Learning to Scale Weight Matrices for Better SVD-Based LLM Compression

• 06-26 中

  SigmaScale 阅读笔记：通过学习缩放矩阵改进 SVD 大语言模型压缩

• 06-25 EN

  ReMP: Low-Downtime Runtime Model-Parallelism Reconfiguration for LLM Serving

• 06-25 中

  ReMP：LLM 推理服务中的低停机运行时并行拓扑重配置

• 06-18 EN

  LUMEN: Load-Aware Coordinated Failure Recovery for Distributed LLM Serving

• 06-18 中

  LUMEN：面向分布式大模型推理的负载感知协同故障恢复

• 06-17 EN

  OScaR: Occam's Razor for Extreme KV Cache Quantization

• 06-17 中

  OScaR：极端 KV 缓存量化的奥卡姆剃刀

• 06-15 EN

  Parallel-Synthesis: Direct KV-Cache Synthesis for Parallel Branches in LLM-Agent Workflows

• 06-15 中

  Parallel-Synthesis：让 LLM 综合智能体直接消费并行分支的 KV 缓存

• 06-14 EN

  GF-DiT: Scheduling GPU Parallelism as a First-Class Resource for Diffusion Transformer Serving

• 06-14 中

  GF-DiT：把 GPU 并行度当作可调度资源的扩散 Transformer 推理系统

• 06-12 EN

  SliceGPT: Post-Training LLM Compression via Computational Invariance

• 06-12 中

  SliceGPT 阅读笔记：用计算不变性删除 Transformer 的行与列

• 06-10 EN

  KeepKV: Lossless KV Cache Compression via Electoral Votes and ZIP-Merging

• 06-10 中

  KeepKV：用「选举票」机制和零扰动合并实现无损 KV 缓存压缩

• 06-08 EN

  ExpWeaver: How LLM Agents Learn from Past Experience in Latent Space

• 06-08 中

  ExpWeaver：LLM 智能体如何在隐空间中从经验中学习

• 06-07 EN

  SlidingServe: SLO-Aware Sliding-Window Scheduling for LLM Inference

• 06-07 中

  SlidingServe：面向LLM推理的SLO感知滑动窗口调度

• 06-04 EN

  Llumnix: Dynamic Scheduling for Large Language Model Serving

• 06-04 中

  Llumnix：大语言模型推理服务的动态调度系统

• 06-03 EN

  KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization — Technical Review

• 06-03 中

  KVQuant：面向千万级上下文的 KV 缓存量化技术——阅读笔记

• 05-29 EN

  IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression

• 05-29 中

  IO-SVD：基于输入输出双侧白化的自适应秩LLM压缩方法

• 05-28 EN

  Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving

• 05-28 中

  Mooncake：以 KV Cache 为核心的大模型推理服务解耦架构

• 05-24 EN

  FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

• 05-24 中

  FlashAttention-2：更好的并行策略与线程块工作划分

• 05-21 EN

  SGLang: Efficient Execution of Structured Language Model Programs — Technical Review

• 05-21 中

  SGLang:为 LM 程序而生的前端 DSL + 协同设计运行时 —— 阅读笔记

• 05-20 EN

  Sarathi-Serve: Taming the Throughput–Latency Tradeoff in LLM Inference — Technical Review

• 05-20 中

  Sarathi-Serve:用 chunked-prefill 驯服 LLM 推理的吞吐-延迟权衡 —— 阅读笔记

• 05-17 EN

  PipeSD: Cloud-Edge Collaborative Pipeline Inference with Speculative Decoding — Technical Review

• 05-17 中

  PipeSD：基于推测解码的云边协同流水线推理框架 —— 阅读笔记

• 05-16 EN

  An Interpretable Latency Model for Speculative Decoding in LLM Serving — Technical Review

• 05-16 中

  用 Little 定律解释推测解码在真实服务中的提速曲线 —— 阅读笔记

• 05-10 EN

  Tutti: Making SSD-Backed KV Cache Practical for Long-Context LLM Serving

• 05-10 中

  Tutti：让基于 SSD 的 KV Cache 真正适用于长上下文 LLM Serving

• 05-09 EN

  Queueing Stability for LLM Inference with KV Cache Memory Constraints

• 04-29 EN

  FEPLB Technical Review: Nearly Free MoE Load Balancing with the NVLink Copy Engine

• 04-24 EN

  Generalization at the Edge of Stability: A Random Dynamical Systems Perspective

• 04-24 EN

  FEPLB: Zero-Cost MoE Load Balancing via NVLink Copy Engine

• 04-22 EN

  SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference Under Hard Uplink Budgets

• 04-19 EN

  SpecGuard: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning

• 04-19 中

  SpecGuard：用于多步推理的验证感知推测解码

• 04-17 EN

  GRASP Technical Review: Replacing Redundant LLM Layers with Adaptive Singular Parameters

• 04-15 EN

  LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding — Deep Technical Review

• 04-15 中

  LayerSkip：让大模型“提前退出 + 自校验推理”成为可部署方案——深度阅读笔记

• 04-10 EN

  SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression — Deep Technical Review

• 04-10 中

  SVD-LLM：面向大语言模型压缩的“截断感知”奇异值分解方法 — 深度阅读笔记

• 04-08 EN

  SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models — In-Depth Technical Review

• 04-08 中

  SmoothQuant：大型语言模型的精准高效训练后量化 — 深度阅读笔记

• 04-03 EN

  AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration — In-Depth Technical Review

• 04-03 中

  AWQ：感知激活值的大模型权重量化压缩与加速 — 深度阅读笔记

• 04-01 EN

  Layer Pruning for Efficient Large Language Models — In-Depth Technical Review

• 03-27 EN

  GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection — In-Depth Technical Review

• 03-25 EN

  GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers — In-Depth Technical Review

• 03-21 EN

  BitNet: Scaling 1-bit Transformers for Large Language Models — In-Depth Technical Review

• 03-14 EN

  FlashAttention: The IO-Aware Algorithm That Made Transformers Actually Fast

• 03-11 EN

  Speculative Decoding: Making LLM Inference 2-3× Faster Without Losing a Single Token

• 02-19 EN

  vLLM and PagedAttention: Efficient Memory Management for Large Language Model Serving — Technical Review

• 02-18 EN

  DeepSeek-V2: Multi-head Latent Attention and DeepSeekMoE — Technical Review