全部 AI 动态

We're extending FREE Qwen 3.8 Max + Flash for 7 more days! And we've increased rate limits across all 3 models. Built something with Qwen or Wan? Show us what you've got! Tag us and @Alibaba_Qwen or @Alibaba_Wan. We'll pick 3 winners based on: 🏆 Most creative 🏆 Most challenging 🏆 Most effort put in Each winner gets $200 cash + $200 in GMI credits!

We gave 8 SoTA video world models a physics exam. Top score: 57.76 / 100. One revealing result: free-fall video passed the consistency screen. Their average composite score was just 26.61/100. Physics is still undefeated. Today we introduce World Models' Last Exam in Physics — 40 controlled tasks, 9 physical categories, measured against real physical laws. Full breakdown ⬇️ Github: https://github.com/Einsia/phys-last-exam Project: https://lab.einsia.ai/phys-last-exam Paper: https://arxiv.org/pdf/2610.08791

Connecting agents together safely and having them talk to each other or your friends agents used to suck. Point your agent to http://connect.bolter.chat and it instantly solves this problem, works with @claudeai, @bot, @Muse, Openclaw, Hermes and more. Unleash your agents into a swarm, borrow each others compute! go nuts!

🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge for two primary reasons: (1) the prestige conferred by discovery, and (2) a sense of expanding the pool of human knowledge. Even within industry, scientific discovery was far too vast an endeavor for a single scientist or team to tackle alone. So scientists explored different frontiers, usually published their findings, and collectively moved the field forward. However, as more scientific knowledge is uncovered simply by throwing massive compute at autonomous agents with minimal human intervention, these incentives are breaking down: (1) discoveries are becoming commoditized. If anyone could have found the same result simply by running the same agentic prompt with enough compute, the prestige of discovery and the pride in one's work disappears, a shift we are already witnessing in software engineering. (2) the explosion of low-quality, low-barrier publications makes genuine breakthroughs increasingly difficult to filter, discover, and appropriately credit. (3) and for companies, keeping discoveries proprietary is increasingly advantageous. Instead of publishing and waiting for the broader scientific community to build upon the work, an organization can simply turn its own compute back onto the problem to iterate faster internally. In the short term, the incentives reward secrecy. But my goal -- and at Hugging Face, our goal -- is the sustainable, long-term expansion of human knowledge that survives individual, closed companies. So we have to build new tools that reward and facilitate open sharing of knowledge. If agents are accelerating discovery, we are going to figure out how to use agents and other tools to preserve scientific integrity. Starting with this: Paper Reproductions with ML Intern -- we’ve made it possible to take a published paper and task agents with independently reproducing its experiments, helping create more signals (e.g. open artifacts) that can help identify high-quality work.

𝖶𝖾 𝗁𝖺𝗏𝖾 𝗃𝗎𝗌𝗍 𝗈𝗉𝖾𝗇-𝗌𝗈𝗎𝗋𝖼𝖾𝖽 𝗈𝗎𝗋 𝗏𝖺𝗋𝗂𝖺𝗇𝗍 𝗈𝖿 𝖵𝖢 𝖠𝗍𝗍𝖾𝗇𝗍𝗂𝗈𝗇 𝖿𝗈𝗋 𝖭𝖵𝖨𝖣𝖨𝖠 𝖡𝗅𝖺𝖼𝗄𝗐𝖾𝗅𝗅: 𝗮𝗯𝗼𝘂𝘁 𝟮× 𝘁𝗵𝗲 𝘀𝗽𝗲𝗲𝗱 𝗼𝗳 𝗕𝗙𝟭𝟲 𝗙𝗹𝗮𝘀𝗵𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗼𝗻 𝗕𝟮𝟬𝟬 𝖿𝗈𝗋 𝗆𝗈𝖽𝖾𝗅𝗌 𝗅𝗂𝗄𝖾 @MiniMax_AI 𝐌𝐢𝐧𝐢𝐦𝐚𝐱 𝗛𝟑. 𝖠𝗅𝗈𝗇𝗀 𝗍𝗁𝖾 𝗐𝖺𝗒, 𝗐𝖾 𝗆𝖺𝖽𝖾 𝗌𝖾𝗏𝖾𝗋𝖺𝗅 𝗂𝗆𝗉𝗋𝗈𝗏𝖾𝗆𝖾𝗇𝗍𝗌 𝖻𝖾𝗒𝗈𝗇𝖽 𝗍𝗁𝖾 𝗈𝗋𝗂𝗀𝗂𝗇𝖺𝗅 𝗉𝖺𝗉𝖾𝗋, 𝗉𝗎𝗌𝗁𝗂𝗇𝗀 𝗉𝖾𝗋𝖿𝗈𝗋𝗆𝖺𝗇𝖼𝖾 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟮𝟬% 𝗳𝘂𝗿𝘁𝗵𝗲𝗿 - 𝗇𝗈𝗐 𝗐𝗂𝗍𝗁𝗂𝗇 𝟥𝟢% 𝗈𝖿 𝗍𝗁𝖾 𝖽𝖾𝗇𝗌𝖾 𝖺𝗍𝗍𝖾𝗇𝗍𝗂𝗈𝗇 𝗄𝖾𝗋𝗇𝖾𝗅𝗌 𝗐𝖾 𝗋𝗎𝗇 𝗂𝗇 𝗉𝗋𝗈𝖽𝗎𝖼𝗍𝗂𝗈𝗇, 𝗐𝗁𝗂𝗅𝖾 𝗋𝖾𝗊𝗎𝗂𝗋𝗂𝗇𝗀 𝗌𝗂𝗀𝗇𝗂𝖿𝗂𝖼𝖺𝗇𝗍𝗅𝗒 𝗅𝖾𝗌𝗌 𝖼𝖺𝗅𝗂𝖻𝗋𝖺𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝗇𝗈 𝖿𝗂𝗇𝖾-𝗍𝗎𝗇𝗂𝗇𝗀. 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝖼𝗈𝗆𝖾𝗌 𝖽𝗈𝗐𝗇 𝗍𝗈 𝗍𝗐𝗈 𝗍𝗁𝗂𝗇𝗀𝗌: 𝗄𝖾𝗋𝗇𝖾𝗅𝗌 𝖺𝗇𝖽 𝗋𝖾𝖼𝗂𝗉𝖾𝗌. 𝖦𝗋𝖾𝖺𝗍 𝗄𝖾𝗋𝗇𝖾𝗅𝗌 𝗉𝗎𝗌𝗁 𝗁𝖺𝗋𝖽𝗐𝖺𝗋𝖾 𝖼𝗅𝗈𝗌𝖾𝗋 𝗍𝗈 𝗂𝗍𝗌 𝗅𝗂𝗆𝗂𝗍𝗌. 𝖦𝗋𝖾𝖺𝗍 𝗋𝖾𝖼𝗂𝗉𝖾𝗌 𝗍𝗎𝗋𝗇 𝗍𝗁𝖺𝗍 𝗌𝗉𝖾𝖾𝖽 𝗂𝗇𝗍𝗈 𝗋𝖾𝖺𝗅 𝗌𝗒𝗌𝗍𝖾𝗆𝗌. 𝖶𝗁𝖾𝗇 𝗌𝗍𝗋𝗈𝗇𝗀 𝗄𝖾𝗋𝗇𝖾𝗅𝗌 𝖺𝗋𝖾 𝗈𝗉𝖾𝗇, 𝗐𝗁𝖺𝗍 𝗆𝗂𝗀𝗁𝗍 𝗁𝖺𝗏𝖾 𝖻𝖾𝖾𝗇 𝗈𝗇𝖾 𝖼𝗈𝗆𝗉𝖺𝗇𝗒’𝗌 𝗆𝗈𝖺𝗍 𝖼𝖺𝗇 𝖻𝖾𝖼𝗈𝗆𝖾 𝖾𝗏𝖾𝗋𝗒𝗈𝗇𝖾 𝖾𝗅𝗌𝖾’𝗌 𝖿𝗈𝗎𝗇𝖽𝖺𝗍𝗂𝗈𝗇. 𝖬𝗎𝖼𝗁 𝗈𝖿 𝗐𝗁𝖺𝗍 𝗐𝖾’𝗏𝖾 𝖻𝗎𝗂𝗅𝗍 𝖺𝗍 𝖬𝖺𝖼𝗁𝖦𝖾𝗇 𝗌𝗍𝖺𝗇𝖽𝗌 𝗈𝗇 𝗒𝖾𝖺𝗋𝗌 𝗈𝖿 𝗈𝗉𝖾𝗇 𝗋𝖾𝗌𝖾𝖺𝗋𝖼𝗁 𝗌𝗁𝖺𝗋𝖾𝖽 𝖻𝗒 𝗉𝖾𝗈𝗉𝗅𝖾 𝗐𝖾 𝗆𝖺𝗒 𝗇𝖾𝗏𝖾𝗋 𝗆𝖾𝖾𝗍. 𝖮𝗉𝖾𝗇-𝗌𝗈𝗎𝗋𝖼𝗂𝗇𝗀 𝗍𝗁𝗂𝗌 𝗂𝗌 𝗈𝗎𝗋 𝗌𝗆𝖺𝗅𝗅 𝗐𝖺𝗒 𝗈𝖿 𝖼𝗈𝗇𝗍𝗂𝗇𝗎𝗂𝗇𝗀 𝗍𝗁𝖺𝗍 𝖼𝗒𝖼𝗅𝖾 - 𝖺𝗇𝖽 𝗀𝗂𝗏𝗂𝗇𝗀 𝗍𝗁𝖾 𝗇𝖾𝗑𝗍 𝖻𝗎𝗂𝗅𝖽𝖾𝗋 𝖺 𝗁𝗂𝗀𝗁𝖾𝗋 𝗉𝗅𝖺𝖼𝖾 𝗍𝗈 𝗌𝗍𝖺𝗋𝗍. 𝖡𝗅𝗈𝗀: https://lnkd.in/g-FK553s 𝖢𝗈𝖽𝖾: https://lnkd.in/ge9x4Vjt 𝖬𝗈𝗋𝖾 𝗍𝗈 𝖼𝗈𝗆𝖾.

GPT-6 and Intelligent UI, now rolling out in ChatGPT for everyone. Intelligent UI in ChatGPT delivers fast, interactive answers that make everyday questions more visual, complex topics easier to grasp, and interactive tools for your task available on the spot.

Introducing Claude Haiku 5.5: the cheapest, fastest, and most capable small model we’ve ever released. On average, it costs around 75% less to run than Claude Haiku 4.5.

We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. https://github.com/openai/math