Alexa Benchmark

The ForgeKernel paper is now released. Last week, we boosted DeepSeek V4.1 FlashMLA real-time by 9.8% using ForgeKernel. Paper: arxiv.org/abs/2611.04998 Check out what the paper includes: 1. Architectural decision-making: We propose an agent architecture that separates architectural design from code implementation, guides implementation through explicit architectural decisions, and revises these decisions based on performance feedback. When optimisation encounters a structural bottleneck, the method revisits work partitioning, dataflow, or pipeline organisation rather than only tuning locally within a fixed architecture. Across runs, it also re-assesses an existing implementation's architecture rather than directly carrying it forward. 2. Measurement-validated mechanism for cross-run self-evolution: Hardware mechanisms discovered during our kernel optimisation are validated through independent probes on the target hardware before being stored in a knowledge base shared across operators and input shapes. The knowledge base guides architectural design and implementation planning in subsequent runs, converting findings from individual runs into reusable knowledge. 3. Introducing ForgeKernel: It is a self-evolving operator optimisation system that integrates architectural revision, reference updates, and knowledge reuse. Evaluation on NVIDIA Hopper and Blackwell GPUs and Huawei Ascend 910C accelerator demonstrates performance gains over expert and generated references, including a 1.10× geometric-mean speedup on NVIDIA GPUs. As of 2026-10-30, submissions generated by ForgeKernel rank first on all four leaderboards: the three CANN-Bench 2.0 solution leaderboards (Ascend 910C, 950DT, and 950PR) and the SOL-ExecBench FlashInfer-Bench track.

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