293 posts in total. Keep on posting.

2026

• 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

  KeepKV 提出了「选举票」机制和零推理扰动合并（ZIP-Merging），在数学上证明了单步无损 KV 缓存压缩，从根本上解决了所有现有合并方法都存在的「注意力衰落」问题。

• 06-09 EN

  VAPO: Value-Augmented Proximal Policy Optimization for Long-CoT Reasoning

  VAPO revives value-model-based RL for LLM reasoning by introducing Length-adaptive GAE and a suite of complementary techniques, reaching 60.4 on AIME 2024 with Qwen2.5-32B — outperforming DAPO by more than 10 points in under 5,000 training steps.

• 06-09 中

  VAPO：面向长链推理的价值增强近端策略优化

  VAPO 通过引入长度自适应 GAE 以及一套互补技术，让基于价值模型的强化学习重新超越了无价值模型方法，在 Qwen2.5-32B 上以不足 5000 步达到 AIME 2024 得分 60.4，比 DAPO 高出 10 分以上。

• 06-08 EN

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

  ExpWeaver replaces text-based experience retrieval with latent-space RAG — encoding past agent trajectories as dense hidden-state vectors and retrieving them at every decoding step via cross-attention, achieving SOTA on 12/13 tasks with 1.5-2x better token efficiency.

• 06-08 中

  ExpWeaver：LLM 智能体如何在隐空间中从经验中学习

  ExpWeaver 用潜空间 RAG 替代文本检索——将智能体的历史轨迹编码为稠密隐状态向量，在每个解码步骤通过交叉注意力检索并融合，在 12/13 个任务上取得 SOTA，同时将词元消耗降低 1.5-2 倍。

• 06-07 EN

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

  SlidingServe introduces a sliding-window-based scheduler that combines a batch latency predictor, dynamic chunking, multi-level priority sorting, and DP-based batch construction to improve LLM serving capacity by up to 30% while cutting SLO violations by 16–53%.

• 06-07 中

  SlidingServe：面向LLM推理的SLO感知滑动窗口调度

  SlidingServe通过批次延迟预测器、动态分块机制、多级优先级排序和基于动态规划的批次构造，在保证SLO的同时将LLM在线推理的服务容量提升最高30%，SLO违约率降低16–53%。

• 06-06 EN

  DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

  DeepSeek-R1 demonstrates that frontier-level mathematical and code reasoning can emerge from pure reinforcement learning without any human-annotated reasoning trajectories, rivaling OpenAI o1 on AIME and Codeforces benchmarks.

• 06-06 中

  DeepSeek-R1：用强化学习激发大语言模型的推理能力

  DeepSeek-R1 证明，前沿级别的数学与代码推理能力可以从纯强化学习中涌现，无需任何人工标注的推理轨迹，在 AIME 和 Codeforces 上与 OpenAI o1 旗鼓相当。

• 06-04 EN

  Llumnix: Dynamic Scheduling for Large Language Model Serving

  Llumnix brings OS-style process rescheduling to LLM serving: it migrates in-flight requests and their KV cache across GPU instances for continuous load balancing, defragmentation, and SLO-aware priority — via a near-zero-downtime pre-copy migration mechanism and a virtual-usage abstraction, achieving up to 15x lower P99 TTFT and 36% cost savings on a 16-GPU cluster.

• 06-04 中

  Llumnix：大语言模型推理服务的动态调度系统

  Llumnix 把操作系统的进程上下文切换思路引入 LLM 推理服务，通过在线迁移请求及其 KV 缓存实现跨 GPU 实例的连续负载均衡——核心是近零停机的预拷贝迁移机制与统一五类调度目标的虚拟用量抽象，在 16 卡集群上将 P99 TTFT 降低最多 15 倍、成本节省 36%。

• 06-03 EN

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

  A deep technical review of KVQuant (NeurIPS 2024), which achieves sub-4-bit KV cache quantization enabling 10M context inference through per-channel key quantization, pre-RoPE quantization, sensitivity-weighted non-uniform datatypes, and per-vector dense-and-sparse outlier handling.