293 posts in total. Keep on posting.

2026

• 05-16 EN

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

  A detailed technical review of Kong et al.'s interpretable latency model for speculative decoding under real serving workloads. Using a roofline-style decomposition plus Little's Law, the paper collapses RPS-versus-latency curves onto a single universal form and gives a mechanistic explanation for why batch=1 SD speedups erode under load.

• 05-15 中

  Zero Sum SVD：用「损失零和」做全局奇异值预算分配的 LLM 压缩方法

  一篇关于 Zero Sum SVD 的中文阅读笔记：把所有层的奇异值堆到一个全局优先队列里，用带符号的损失敏感度和「零和守恒」的贪心规则一次性决定全模型的秩预算，让异质化的逐层秩自然从一条标量约束里掉出来。

• 05-15 EN

  Zero Sum SVD: A Global, Loss-Aware Rank Budget for LLM Compression

  A detailed technical review of Zero Sum SVD, which replaces per-layer rank optimization with a global, signed loss-sensitivity heap and a greedy zero-sum rule, letting heterogeneous per-layer ranks fall out of one scalar conservation law.

• 05-14 中

  DisagMoE：用解耦 Attention 和 FFN 打通 MoE 训练的 all-to-all 瓶颈

  一篇关于 DisagMoE 的中文阅读笔记：把 attention 和 FFN 分别放到独立 GPU 池，用 AF-Pipe 调度和 M2N 通讯原语把两侧拼起来，从而把 MoE 训练里的 all-to-all 瓶颈藏进计算之下。

• 05-14 EN

  DisagMoE: Disaggregating Attention and FFN to Beat the MoE All-to-All Bottleneck

  A detailed technical review of DisagMoE, which disaggregates attention and FFN layers onto separate GPU pools and stitches them together via the AF-Pipe schedule to hide the MoE all-to-all bottleneck during training.

• 05-12 中

  DAPO：大规模开源 LLM 强化学习系统

  一篇关于 DAPO 的中文阅读笔记：它把 Clip-Higher、动态采样、token-level loss 与 overlong reward shaping 组合成可复现的大规模 LLM 强化学习配方。

• 05-12 EN

  DAPO: An Open-Source LLM Reinforcement Learning System at Scale

  A detailed technical review of DAPO, an open-source large-scale reinforcement learning recipe for reasoning LLMs using Clip-Higher, dynamic sampling, token-level loss, and overlong reward shaping.

• 05-11 中

  MASPO：面向 LLM 多智能体系统的联合提示词优化

  一篇关于 MASPO 的中文阅读笔记：它用 local、lookahead 与 global 三类信号联合优化 LLM 多智能体系统中的角色提示词。

• 05-11 EN

  MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems

  A detailed technical review of MASPO, a joint prompt optimization method for multi-agent LLM systems that balances local, downstream, and global rewards.

• 05-10 中

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

  一篇关于 Tutti 的中文阅读笔记：它从 GPU-native KV cache object store、GPU io_uring 与 slack-aware scheduling 出发，让 SSD-backed KV cache 更适合长上下文 LLM serving。

• 05-10 EN

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

  A detailed technical review of Tutti, a GPU-centric SSD-backed KV cache system that makes long-context LLM serving cache reuse practical.

• 05-09 EN

  Queueing Stability for LLM Inference with KV Cache Memory Constraints

  A detailed technical review of a queueing-theoretic framework for predicting LLM inference stability under KV cache memory constraints.