291 posts in total. Keep on posting.

2026

• 08-06 中

  MoE 计算-通信细粒度重叠：用 Tile 级信号把 All-to-All 藏在 GEMM 背后

  一个不做内核融合的 producer-consumer 设计：用 tile 级设备驻留信号、远端归属对齐的行布局、以及互不干扰的 SM 分区，把 MoE 第二次 all-to-all 的通信开销藏进专家计算里，在 4 张 A100 上拿到最高 2.74 倍 MoE 层加速。

• 08-05 EN

  AnchorKV: Anchor-Residual KV Cache Compression

  AnchorKV keeps every single token in the KV cache alive at 20x compression by projecting each token onto its nearest exactly-stored anchor and spending a small residual budget only where it moves the attention output the most.

• 08-05 中

  AnchorKV：锚点-残差表示下的 KV 缓存压缩阅读笔记

  AnchorKV 把每个 token 都投影到离它最近的一个精确保留的『锚点』上，再把剩余的字节预算精准花在最影响注意力输出的残差上，在 20 倍压缩比下一个 token 都不丢，精度还比驱逐类方法高一大截。

• 08-04 EN

  GRPO vs. Dr. GRPO Is Not a Bug-Fix Story: An Impossibility Theorem for Length Bias in Group RL

  A short, sharp impossibility theorem shows that no length-based weighting scheme for group-relative policy optimization can be simultaneously gradient-unbiased and length-invariant — GRPO and Dr. GRPO are two ends of one unavoidable Pareto tradeoff, not a broken algorithm and its fix.

• 08-04 中

  GRPO 和 Dr. GRPO 不是「修复」关系：一个关于长度偏差的不可能性定理

  一篇短小精悍的不可能性定理证明：任何只依赖长度的加权方案都无法同时做到梯度无偏和长度不变——GRPO 与 Dr. GRPO 只是同一条不可避免的 Pareto 权衡曲线上的两个端点，而不是一个算法和它的修复版。

• 08-03 EN

  BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning

  Self-doubt words like 'Wait' are not the only place a reasoning model already has the right answer — BLADE shows that ordinary sentence boundaries hide most of the early-exit opportunity, and picking the right hidden layers matters as much as picking the right checkpoint.

• 08-03 中

  BLADE 阅读笔记：把「早退」这件事从自我怀疑词扩展到普通句子边界

  推理模型早就写出了正确答案，却还要靠「Wait」这种自我怀疑词才敢喊停——BLADE 发现真正的早退机会大多藏在普通句子边界里，选对探针层和选对检查点一样重要。

• 08-02 EN

  FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training

  MoE routing imbalance wastes 18.6% of GPU time even without an auxiliary loss — FEPLB shows the fix does not need a new communication path, it needs the NVLink Copy Engine hardware that current frameworks leave idle.

• 08-02 中

  FEPLB 阅读笔记：用 Copy Engine 几乎零成本地做 MoE 负载均衡

  即使不加辅助负载均衡损失，MoE 路由不均也会白白浪费 18.6% 的 GPU 时间——FEPLB 的答案不是设计更聪明的调度算法，而是发现 Hopper 架构上一直闲置的 NVLink Copy Engine 硬件。

• 08-01 EN

  Back from the Future: KV Cache Management by Counter-Causal Surprise

  A training-free KV-cache eviction rule that asks a strange but powerful question -- can the future predict this token? -- and in doing so escapes the self-reinforcing bias that quietly poisons every attention-score-based eviction method.

• 08-01 中

  来自未来的信号：用反因果惊奇度做 KV Cache 淘汰

  一个不需要训练的 KV Cache 淘汰策略，用一个反直觉的问题——这个 token 能被它后面的内容预测出来吗——绕开了几乎所有基于注意力分数的淘汰方法都存在的自我强化偏差。

• 07-31 EN

  DynaCalKV: Rethinking Fixed Head Grouping in Low-Rank KV Cache Compression

  Fixed group sizes for attention-head clustering in low-rank KV cache compression turn out to be a needless assumption — letting similarity structure itself decide the groups saves parameters, but only if you already have enough independent heads to cluster.