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A user comments on a live demonstration of real-time recursive self-improvement (RSI) applied to kernel optimization, praising the measurable gains and the boldness of putting the self-improvement loop on screen.
OpenBMB 正在直播展示 ForgeKernel 的 RSI 实时内核优化过程，相关站点与直播链接已公开。
A technical analysis of optimizing LLM inference using agent fleets, based on DeepSeek 4.1 Flash optimization reaching 2300 tok/s, highlighting that most gains come from configs/graphs rather than new kernels, questioning megakernel approaches, and emphasizing verification and convergence in automated optimization runs.
该论文提出 FlashNext-RL——一个针对 Qwen3.8-Flash-Next 的模型专用长时程 agentic RL 训练优化框架，基于 prime-rl 与 vLLM，在单个 8×B200 节点上将 BF16 全模型训练吞吐从约 395 tokens/s/GPU 提升至 4,438 tokens/s/GPU（131K token 上下文），并支持实时 SWE RL 训练运行。
论文提出联合子空间稀疏注意力（JSSA），通过学习共享低维查询-键子空间并设计免gather的索引注意力核，在128K上下文下实现最高6.43倍解码吞吐提升，同时保持接近全KV的精度。
DuoDiscover 是一个自主发现搜索系统，通过让编码代理协同发现优化目标与快速代理评估器（yardstick），并用保真度测试防止代理指标漂移，来突破自主发现中的评估瓶颈。在 SWEfficiency、SOL-Exec、vLLM、SQLite 及 CPU 微架构优化等多个基准上取得显著成果，部分任务上超越 AlphaEvolve 系统与人类专家。
ROCmFPX 的融合 HC norm 内核通过在寄存器中直接写出 Q8_1 副本，省去了 MMQ 重复量化 335MB 数据的开销，在 2.3%–3.6% 的 pp8192 性能提升下保持位级一致。
ROCmFPX's gated delta net prefill kernel was optimized to process 16-token chunks via RDNA3 WMMA instead of one token at a time, cutting latency from 772µs to 610µs per 512-token call and improving prompt-processing throughput by ~2-4% across configurations, with Strix Halo testing ongoing.
ROCmFPX 更新了融合 HC norm 的匹配器，使其能识别 qwen4exp 将 REPEAT/MUL/ADD 合并为单一 op 的组合，从而将 HC 内核每 2048 token 耗时从 416 ms 降至 175 ms。相关修复还解决了导致 llama-perplexity 崩溃的 xn 别名问题，并在 BF16 拷贝路径上移除了变慢的 Q8_0 MMB down proj。
RunInfra announces a major release of GLM 5.3 Flash with rewritten inference kernels, delivering 670 tok/s on an FP8, vendor-native build now also running on AMD, with competitive pricing and a 1M token context window.
The article describes how to use the 'make tune-kernels' tool in Splash to optimize AI model decoding on 40-core M5 Max chips, achieving up to 20% speed improvement by tuning kernel layouts for specific hardware.
An agentic CUDA kernel optimizer that automates GPU implementation generation through iterative code generation, correctness checks, benchmarking, and refinement, powered by LangGraph and OpenAI models.
This Twitter thread introduces Hugging Face's Kernels, a tool that allows users to select and replace optimized kernel implementations for supported layers in AI models without rewriting the entire model.
The article describes an optimization for the DeepSeek-V4.1-Flash AI model on Metal hardware, where the attention kernel was improved to only launch necessary tiles, reducing compute waste and enhancing inference speed.
The paper presents tritonBLAS, an analytical model for optimizing GPU GEMM kernel parameters without runtime autotuning, achieving near-optimal performance and significantly reducing compilation time.
The article introduces AMDKernelVault, an open dataset and training framework for AMD GPU kernel optimization, featuring large-scale HIP and Triton kernels and agent-driven pipelines for generating and validating kernels.
Google Accelerator Agents is a GitHub repository of AI-powered tools to accelerate machine learning development on TPUs, featuring agents for code migration and kernel optimization using Gemini.
This paper introduces DataKernelBench, a benchmark for evaluating LLMs on optimizing GPU kernels for database queries, achieving speedups over baseline methods like torch.compile.
KernelArc is a multi-agent framework that uses strategy-specialized agents to autonomously optimize GPU kernels across heterogeneous workloads, achieving top rankings on NVIDIA GPU benchmarks.
This paper presents CAKE, a compiler-agent co-design framework that lets AI agents author a hardware-explicit IR for GPU kernels, achieving significant speedups over baselines across various workload families.