实验室伙伴的辛苦工作!
🤗 MOSS-Image is now open source on @huggingface .
Built on a 5B Diffusion Transformer (DiT), MOSS-Image brings Chinese & English text rendering and controllable layouts to image generation — from posters and infographics to creative visuals.@Open_MOSS
What it offers:
📝 Long prompts & text rendering — up to 4K-token prompts, including multi-line text
📐 Experimental JSON layout control — specify text, objects, and their positions using bounding boxes
🖼️ Flexible generation — multiple aspect ratios, resolutions, and customizable inference settings 🛠️ Developer workflows — Python (Diffusers), Gradio, ComfyUI, and a Prompt Enhancer Skill
📦 Base & SFT checkpoints are both available, with open model weights and Apache-2.0 licensed inference code.
🙌 Huge thanks to @sgl_project @vllm_project @ModelScope2022 for the Day-0 support!
This is so good for me now!
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
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Yitong chen retweeted
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
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natural idea!
Excited to share our paper: Image Tokenizers Should Be Trained Between Images
TL;DR: We supervise the space between image codes. Far fewer artifacts between images, and generators train faster and generate better.
Thanks @JDihlmann & Hendrik Lensch @CG_Tuebingen!
Links 👇
Yitong chen retweeted
In LLM post-training, policy gradient is "the RL loss".
When it fails, we blame the RL: exploration, credit assignment, sampling noise.
Image classification has none of these, and the policy gradient is exact. Following it is terrible...
ImageNet, 4% vs 62% for cross-entropy.
funny🤣
The LIBERO dataset shows up in many robot-learning benchmarks. Curious what the data behind the benchmark numbers actually look like?
We converted LIBERO to RRD, so you can explore the tele-operated demonstrations directly with Rerun.
Pick a task and watch the motion in 3D alongside camera views and robot states. Explore the data using our query APIs. Train on it using our dataloader.
Data and code 👇
化神级?
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense.
- Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model
- A major leap in cybersecurity, setting a new standard among open models
Tech Blog: z.ai/blog/glm-5.3
Yitong chen retweeted
Building on GLM-5.2’s contributions to cyber defense, GLM-5.3 delivers substantially stronger capabilities in vulnerability discovery, exploit analysis, and complex, multistep security tasks in realistic environments. These advances can help defenders identify weaknesses earlier, validate mitigations, and respond more effectively to emerging threats.
Select partners now have access under strict safeguards. Broader access and API availability will follow rigorous safety and security evaluations. We intend to release GLM-5.3 as an open-weight model once those evaluations are complete and appropriate safeguards are in place.
We are working with independent security experts and leading institutions to assess these risks, strengthen safeguards, and determine an appropriate path forward.
qwen3-vl 做 multimodal encoder 到不奇怪,可惜没有看到 MiniMax H3 这种 diffusion generative model 来提升 vlm 能力的
Excited to see Qwen3-VL-32B being used as a multimodal encoder in MiniMax H3.
This is a great signal that multimodal foundation models are evolving beyond standalone VLM benchmarks — becoming core building blocks for next-generation generative and agentic systems.
From perception to creation, multimodal intelligence is entering a new stage.
让我们看看机器队的表现
yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model.
We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann algebras (disproof of Connes' Rigidity Conjecture) to better bounds for high dimensional sphere packing, for circuit complexity, for monochromatic triangles in multicolored graphs, and more.
More thoughts here: openai.com/index/ten-advance…
v4-flash 几乎算本地model了,nb
🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!
🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇
🔷 The official V4-Flash now natively supports the Responses API format and is fully adapted for Codex!
Check out the configuration details in our official API docs: api-docs.deepseek.com/quick_…
again?
Dinitz-Garg-Goemans conjecture is false. This graph theory problem was open for ~30 years.
The graph below has fractional flow cost 58. Any unsplittable flow (with capacity violation <=15) has cost at least 60.
Chat with GPT 5.6 Pro where this was found: chatgpt.com/share/6a60b2eb-0…
Yitong chen retweeted
憋了太久了,终于能聊 k3!
天天被问,现在可以正面回答,Kimi Code 是用 K3+Kimi Code 构建的,用多了 K3,感觉 claude/gpt有点流口水(菜)
好消息:K3 终于发了,大家可以一起嗨了
坏消息:我得跟大家一起抢 K3 的容量了😭,我已急哭
🛫🛫🛫
Yitong chen retweeted
🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length.
🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.
🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice.
Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today!
📄 Tech Report: huggingface.co/deepseek-ai/D…
🤗 Open Weights: huggingface.co/collections/d…
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Yitong chen retweeted
Fun project at PI: knowledge insulation for VLAs. We figured out how to train VLAs with cont. actions much more effectively by insulating the VLM and training it with discrete actions, while action expert learns on top. 5-7x faster, and importantly way better language following 👇






























