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Reasoning = step-by-step thinking. Usually. Diffusion reasons in parallel and refines natively. Better quality without the latency tax. Samar Khanna on how diffusion reasoning works. We're hiring →

Inception

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16,490 görüntüleme • 6 ay önce •via X (Twitter)

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Aida profil fotoğrafı

Aida6 ay önce

@samar_a_khanna Isn't diffusion more FLOPS heavy than AR? How come it works faster than AR then. I understand the hardware utilization is going to be higher, but with 20x faster generation, you need at least 20x higher hardware utilization to get the speed up.

HTV profil fotoğrafı

HTV6 ay önce

@samar_a_khanna Idea and speed is super cool,

Ranko Mosic profil fotoğrafı

Ranko Mosic6 ay önce

@samar_a_khanna

Benzer Videolar

Listen to Samar Khanna explain why parallel generation, rather than sequential, raises the performance ceiling for language models. Learn more about diffusion LLMs. → We're hiring:

Listen to Samar Khanna explain why parallel generation, rather than sequential, raises the performance ceiling for language models. Learn more about diffusion LLMs. → We're hiring:

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We're moving beyond autoregressive LLMs! Autoregressive LLMs generate text word-by-word, which can be slow and affect quality, while diffusion models refine noise step-by-step, allowing for faster iterations and error correction. Here's Gemini Diffusion running at 857 tokens/s:

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🚨xAI: GROK 3 ALLOWS YOU TO FOLLOW ITS REASONING STEP-BY-STEP "Part of Grok's advanced reasoning capabilities are these thinking traces that you can see. You can even go inside and actually read what Grok is thinking as it's going through the problem, as it's trying to solve it." Source: xAI Elon Musk

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Video might be the next intelligence substrate. Strikingly, video models are beginning to exhibit the same emergent reasoning behaviors first observed in LLMs—multi-path search, self-correction, and layer specialization. We demystify video reasoning and show it doesn’t happen frame-by-frame, but along diffusion steps. 🔗 📄 So, what's next? ;)

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机器之心 JIQIZHIXIN

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AK

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Code Arena can handle image inputs for agentic web dev tasks, reasoning through multi-step problems and using tools along the way. Watch how it works with Aryan Vichare in this clip. Find a link to the full walkthrough on how Code Arena creates sites and apps from images in thread.

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ComfyUI

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How can we get VLMs to move their eyes—and reason step-by-step in visually grounded ways? 👀 We introduce ViGoRL, a RL method that anchors reasoning to image regions. 🎯 It outperforms vanilla GRPO and SFT across grounding, spatial tasks, and visual search (86.4% on V*). 👇🧵

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I'm thrilled to announce the launch of ⚡️Flash Diffusion from Jasper! Earlier this year, with our acquisition of Clipdrop, we launched the Jasper AI Research Lab in Paris. Today, we are excited to release our first piece of groundbreaking research: the open-source distillation method, "Flash Diffusion". Flash Diffusion accelerates inference by 500%, reduces computing costs, and produces higher-quality image outputs. Dive into the details and discover how Flash Diffusion is set to revolutionize the field of AI and image synthesis. Read all about it here: Try a demo on Hugging Face:

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Martin Lewis

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SGLang-Diffusion with VDN-H3 now generates 14.4s of 768p video in just 9.0s 🚀 On 8× B200, 8 step denoising takes just 6.9s, reaching over 2× real time. The 9.0s figure covers the full generation request after warmup. No measured quality regression versus dense 50-step H3 across 103 test prompts. 🧵

SGLang-Diffusion with VDN-H3 now generates 14.4s of 768p video in just 9.0s 🚀 On 8× B200, 8 step denoising takes just 6.9s, reaching over 2× real time. The 9.0s figure covers the full generation request after warmup. No measured quality regression versus dense 50-step H3 across 103 test prompts. 🧵

SGLang

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🚀 Introducing Emu3.5 — a large-scale multimodal world model that natively predicts the next vision-language state. 🔥 Trained on over 10T interleaved vision-language tokens and enhanced with reinforcement learning, Emu3.5 achieves powerful multimodal reasoning and generation. ⚡ Powered by our new Discrete Diffusion Adaptation (DiDA) for 20× faster inference. 🔥 Emu3.5 outperforms Nano Banana across image generation, editing, interleaved tasks and more. 🌍 Explore Emu3.5: Github: #Emu3 #MultimodalAI #WorldModel #NextTokenPrediction

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