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[1/N]🚀New decoding paradigm drop!🚀 Introducing Lookahead Reasoning(LR): step-level speculation that stacks with Speculative Decoding(SD). It has been accepted to #NeurIPS2025 🎉 📖 Blog: 💻 Code: 📄 Paper:

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Hao AI Lab

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43,199 görüntüleme • 1 yıl önce •via X (Twitter)

EğitimBilim & Teknoloji#NeurIPS2025
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We’re open-sourcing lithos-metal 🚀 Megakernels + DSpark speculative decoding run Qwen3.8-27B at 200+ tokens/s/user peak on one Apple M5 Max. Ultra-fast inference on your laptop. Try it with any coding agent in one command. Code: Tech blog:
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We’re open-sourcing lithos-metal 🚀 Megakernels + DSpark speculative decoding run Qwen3.8-27B at 200+ tokens/s/user peak on one Apple M5 Max. Ultra-fast inference on your laptop. Try it with any coding agent in one command. Code: Tech blog:

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Introducing MiniCPM 4.1-8B: First Open-Source Reasoning LLM with Trainable Sparse Attention ✅ Strong Reasoning Capability: Surpasses similar-sized models on 15 tasks! ✅ Fast Generation: 3x decoding speedup for reasoning ✅ Efficient Architecture: Trainable sparse attention, frequency-ranked speculative decoding Download Models: Huggingface: Github: Technical Report: #AI #MiniCPM #LLM #OpenBMB #ArtificialIntelligence #MachineLearning
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Introducing MiniCPM 4.1-8B: First Open-Source Reasoning LLM with Trainable Sparse Attention ✅ Strong Reasoning Capability: Surpasses similar-sized models on 15 tasks! ✅ Fast Generation: 3x decoding speedup for reasoning ✅ Efficient Architecture: Trainable sparse attention, frequency-ranked speculative decoding Download Models: Huggingface: Github: Technical Report: #AI #MiniCPM #LLM #OpenBMB #ArtificialIntelligence #MachineLearning

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🚀 Introducing #SparseVideoGen: 2x speedup in video generation with HunyuanVideo with high pixel-level fidelity (PSNR = 29)! No training is required, no perceptible difference to the human eye! Blog: Paper: Code: #ArtificialIntelligence #OpenSource [1/7]

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🎉 MiniMax M3.1 Flash from MiniMax (official) has Day-0 support in vLLM! Built for multi-step automation, real-codebase engineering and visual reasoning—with native NVFP4 KV, NVFP4 MoE weights and DSpark speculative decoding in the serving stack. What the model can do, and the vLLM engineering that makes it practical to serve. 👇 Model: 1/5

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🚀 Big news from Recursion! Our 2nd Neuroscience Phenomap — a whole-genome map of the brain’s immune cells 🧠 — has been accepted by Roche & Genentech, triggering a $30M milestone! A massive step toward decoding biology to radically improve lives. 💥 #TechBio #AI #Neuroscience
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🚀 Big news from Recursion! Our 2nd Neuroscience Phenomap — a whole-genome map of the brain’s immune cells 🧠 — has been accepted by Roche & Genentech, triggering a $30M milestone! A massive step toward decoding biology to radically improve lives. 💥 #TechBio #AI #Neuroscience

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🎬 VChain is accepted to #ACL2026 Findings! 🚀 #VChain is an inference-time framework that injects reasoning signals (*Chain-of-Visual-Thought*) from MLLMs to guide video generation for better consistency. 🔗 Website: 📄 Paper:
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🎬 VChain is accepted to #ACL2026 Findings! 🚀 #VChain is an inference-time framework that injects reasoning signals (*Chain-of-Visual-Thought*) from MLLMs to guide video generation for better consistency. 🔗 Website: 📄 Paper:

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0:54

🚀 Our DSpark for Qwen3.8-27B beats native MTP with the same 8 speculative tokens on 4×H200. Up to 52% faster single-stream decoding and 23% higher peak throughput. On 8-needle MRCR, it averages 4.30 accepted tokens beyond 1M context. Model: Demo below, see for yourself!

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Steps to watch this video?👇🤩 Step 1 - Video is only for fun.. No Decoding, Superfans! 🥳 Step 2 - Video is only for fun.. No Decoding, Superfans! 🥳 Step 3 - Video is only for fun.. No Decoding, Superfans!🥳 #WhistlePodu with Tamil Nadu Weatherman

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Fast Byte Latent Transformer is accepted to ICML 2026! ⚡🥪 Byte-level LMs promise to free us from subword tokenizers, but decoding one byte at a time is super slow. We make BLT generation more efficient with BLT-D: text diffusion for parallel byte decoding. 1/

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While exploring diffusion language models (DLMs), we kept asking ourselves: given speculative decoding, a lossless acceleration technique, why do we still need DLMs? 🤔 Our latest work, ORL, tries to answer this question: AR optimizes intelligence 👆, speculative decoding optimizes speed 👉, and DLMs optimize the Pareto frontier of speed and intelligence 🚀. Through co-design across the algorithms, the training and inference engine, and the kernels, ORL pushes out a new Pareto frontier of intelligence and speed. Similar performance, much faster than speculative decoding. You say lossy spec? ORL-DLM is better too! 😎 🌐 Project + live demos:
1:28

While exploring diffusion language models (DLMs), we kept asking ourselves: given speculative decoding, a lossless acceleration technique, why do we still need DLMs? 🤔 Our latest work, ORL, tries to answer this question: AR optimizes intelligence 👆, speculative decoding optimizes speed 👉, and DLMs optimize the Pareto frontier of speed and intelligence 🚀. Through co-design across the algorithms, the training and inference engine, and the kernels, ORL pushes out a new Pareto frontier of intelligence and speed. Similar performance, much faster than speculative decoding. You say lossy spec? ORL-DLM is better too! 😎 🌐 Project + live demos:

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I’m excited to announce that 💫StarVector has been accepted at CVPR 2025! Over a year in the making, StarVector opens a new paradigm for Scalable Vector Graphics (SVG) generation by harnessing multimodal LLMs to generate SVG code that aesthetically mirrors input images and text. With this milestone, we’re also releasing StarVector on Hugging Face! 🥳🚀

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