全部 AI 动态

ChatGPT, Claude, Codex, and Grok experiencing service disruptions within the same window. OpenAI, Anthropic acknowledged elevated errors across ChatGPT, Codex, and Claude.

GPT-6 Astra is coming to Devin. On FrontierCode 1.1, Astra performs within 0.4 points of Fable 5 at a 64% lower cost. It also sets a new SOTA on our internal testing benchmark, generating more comprehensive tests, clearer reports, and better video evidence.

由 @OpenAI 推出的 GPT-6 Astra 在 ARC-AGI 上达到 SOTA： - Astra 在 ARC-AGI-3 上得分 63%，通过新的提供商适配器测试框架可达 99% - 它在 96% 的 ARC-AGI-3 关卡上超越了人类表现 - 它构建了我们所见过的、对新环境最精确的符号模型 我们的分析：

Luna is the first closed source model in a long time to top the OpenRouter charts! Great job to the OpenAI team! Incredible combination of price and performance.

GPT-6 Astra debuts on APEX-Agents at #1 on the leaderboard. 🥇 62.4% Mean score (#1) 🥈 46.7% Pass@1 (#2) It leads Fable 5.1 by 0.4 points on mean score and trails it by 0.8 on Pass@1. APEX-Agents runs long-horizon tasks written by bankers, consultants, and corporate lawyers in our expert network. Against its predecessor GPT-5.6 Sol, Astra moves up 6 places on our leaderboard. It gains 5.7 points on mean and 6.7 on Pass@1. All three domains moved by at least 4.5 points, so this is a broad gain rather than one job pulling the average up. Congratulations to @OpenAI. See full leaderboard: https://www.mercor.com/apex/apex-agents-leaderboard/

OpenAI 的发布视频越来越出色了。

当我们发布 ARC 3 时，有人问我：“你觉得前沿模型什么时候能把它刷满？”我回答说：“大约一年吧，不过这取决于它被专门针对的程度。” 那是 6 个月前的事了，所以 Astra 所代表的进展大约比我预想的快了 2 倍。我认为进展的速度会让很多人吃惊，而新模型能做到的事，将会挑战人们通过使用前几代模型所形成的对 AI 的看法。

Another fun demo of Astra, I realized like 3.5 hours ago I didn't have a cool enough demo In 75 minutes, I gave it the same simulate macOS 27 prompt. It's BY FAR the best output I've seen. If you log in, it also has cloud sync and working apps https://macos-27-simulator.mweinbach.chatgpt.site/

Any smart human giving it real effort should score >90% on ARC-AGI-3

🤖🧠NEW PAPER🧠🤖 (The result of an 8-year project!) LLMs seem very different from symbolic systems. Yet LLMs excel in symbolic domains (e.g., language/code/math). How do they do it? Our finding: LLM representations have implicit symbolic structure! Link in thread ⬇️ 1/n

Satya Nadella: Microsoft’s latest Wisconsin AI data center keeps yearly water consumption no higher than that of 1 local restaurant. "The cooling loop is filled once and the data centre can operate effectively with zero water consumption. Daily water usage across a year is roughly equivalent to what a single restaurant would use" The mechanism is mainly about replacing evaporative cooling with closed-loop direct-to-chip liquid cooling, so water moves like coolant inside a sealed machine rather than being boiled off into the air. Hot GB200-class AI racks produce too much heat for normal air cooling, so cold liquid is pushed through pipes into the servers and across metal cold plates touching the hottest chips. The liquid enters the rack cool, absorbs heat from the chips through cold plates, then exits the rack at a higher temperature and carries that heat through pipes to a huge cooling system outside the compute floor. Microsoft says Fairwater sends that hot water to cooling “fins” beside the datacenter, where 172 20-foot fans blow air across the fins and dump the heat into the outside air. The important detail is that the air cools the water through metal surfaces, so the water does not need to evaporate the way many older datacenters use cooling towers. The cooled liquid then returns to the servers, repeats the loop, and keeps absorbing heat from the chips. In older data centers, heat is often removed partly through cooling towers. Hot water meets moving air, some water evaporates, and that phase change carries heat away. Effective, but it consumes fresh water continuously. But Firwater is a closed loop because the same coolant keeps circulating through sealed pipes: it absorbs heat from the chips, releases that heat through radiator-like fins, then flows back to the chips again. For Wisconsin Fairwater, Microsoft says more than 90% of the facility uses closed-loop liquid cooling, while the remaining portion uses outside air and switches to water only on the hottest days. ---- From "Microsoft" YouTube channel, (link in comment)

A massive win for OpenAI (and for AI training in general) for its legal case against New York Times. The U.S. govt just formally backed OpenAI’s claim that copyrighted-text training is fair use, partly on national-security grounds. This is Washington’s first formal intervention in the wider wave of copyright lawsuits over AI training, though the filing is advisory rather than binding on the court. The U.S. Dept of Justice filed a statement of interest of the US formally arguing in the OpenAI copyright litigation that training LLMs on copyrighted texts should generally qualify as fair use; That distinction still leaves separate copyright questions around how training data was acquired and whether particular outputs reproduce protected passages. The Justice dept separated acquiring material, training on it, and generating outputs, then focuses its argument specifically on copying at the training stage. It argues that training serves a different purpose from publishing an article because an LLM uses text to learn statistical relationships and generate new responses. For market harm, DOJ says training itself does not substitute for the original work, so later AI-generated competition should not automatically make the earlier training unlawful. The administration also warns that blanket licensing requirements could raise barriers for smaller AI companies and put U.S. developers at a disadvantage against foreign competitors. The court must still decide fair use case by case, but adopting DOJ’s framework would shift much of the legal pressure from model training toward data acquisition and specific outputs.