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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.

Big scenes rarely come down to one sound, so we built SFX v3 to handle more than ever. Our latest model lets you create richer, more precise sound effects from a single text prompt, with improved music and vocals. Now available inside @ElevenCreative and ElevenAPI.

Today, @JudgmentLabs opens for general access. We believe long running agent systems will generate more exhaust data than the world can currently process. Hidden within agent traces are rich records of how to make these systems better: what users ask for, when agents stumble, and how agents interact with the external world. Judgment is the most performant monitoring and improvement layer for long-horizon agents. We’re excited to see what you build!

Your OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: github.com/OpenBMB/MiniCPM-O… 🤗 Hugging Face: MiniCPM-OCR 1.0：huggingface.co/openbmb/MiniC… MiniCPM-OCR Jev：huggingface.co/openbmb/MiniC… 👉 Try the Demo：platform.modelbest.ai/consol… Use the API：platform.modelbest.ai/consol…