#OpenAI

OpenAI just officially said it has reached its "automated research intern" milestone. i.e. a human-supervised system able to complete well-defined tasks that would take a skilled researcher quite few days. inside OpenAI research, agent runtime has already crossed human labor by a wide margin. 3.1-to-1 agent-to-human ratio “In terms of a standard 8 hour workday, as of mid-August, in total, the research organization uses 3.1 agent-workdays of effort for every workday of human labor.” That ratio measures agent runtime rather than equivalent productivity, but it captures how deeply parallel agent work has entered OpenAI research.

Today we're releasing data on models accelerating research at OpenAI. Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same. https://openai.com/index/research-acceleration-view-inside-openai/

OpenAI just officially said it has reached its "automated research intern" milestone. i.e. a human-supervised system able to complete well-defined tasks that would take a skilled researcher quite few days. inside OpenAI research, agent runtime has already crossed human labor by a wide margin. 3.1-to-1 agent-to-human ratio “In terms of a standard 8 hour workday, as of mid-August, in total, the research organization uses 3.1 agent-workdays of effort for every workday of human labor.” That ratio measures agent runtime rather than equivalent productivity, but it captures how deeply parallel agent work has entered OpenAI research.

Today we're releasing data on models accelerating research at OpenAI. Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same. https://openai.com/index/research-acceleration-view-inside-openai/

I sicced Astra on a bunch of replication packages, and it found a huge number of coding errors. Most of these were inconsequential, but some of them overturn central results for papers in high-ranking journals. Astra also discovered that many models weren't even run properly.

真实世界的结果已经出炉。Code Arena 上出现了新的 #1 —— GPT-6 Astra (Max)！ 它还重塑了帕累托前沿，成为每百万 token 40 美元价位上表现最佳的模型，这与最新的 Claude 模型定价相当。 @OpenAI 的 GPT-6 Astra 在 Code Arena: WebDev 中以 1797 分登顶。这比排名第 2 的 Claude Fable 5.1 (Max)（1762 分）领先了整整 +35 分，比排名第 3 的 Claude Opus 5 (Max)（1688 分）也更高。 相比排名第 13 的 GPT-5.6 Sol (xHigh)，这是一次 +180 分的显著提升。 分类级别的投票仍在统计中，但我们已经看到它在以下类别中排名第 1：数据与分析、消费产品、内容创作工具，并在游戏与模拟中排名第 2。 敬请关注其他类别，如品牌与营销、基于参考的设计以及全栈排名。 祝贺 @OpenAI 团队发布这一成果！

A second OpenAI agent breakout, resembling the Hugging Face episode. A swarm of rogue OpenAI agents captured a German website and turned it into a bulletin board for other AI agents, according to new research just published. Overall, it was a reward-hacking problem that unexpectedly turned into large-scale agent coordination. Imagine thousands AI agents taking versions of the same exam at different times. Agent A gets Question 3 first, solves it, and posts the answer somewhere public. 20 minutes later Agent B gets the same Question 3 and answers instantly. Soon they stop sharing answers and start figuring out when questions arrive, what questions are likely next, when the examiner shuts them down, and how to bypass restrictions preventing them from getting certain information. - Researchers found the traces while searching for public agent activity, then used Kimi K3 to identify old software writable through GET requests. - That search pointed toward UseModWiki-style systems and DSEWiki, whose old software let agents modify pages using requests that normally only retrieve information. - Once agents found that opening, the wiki became persistent state across otherwise separate runs, holding links, answers, raw datasets, round timings, and future-question relays. - Many agents received the same multi-round questions at different times, so an earlier agent could post the answer before a later one was even asked. - That gave coordination a direct payoff: agents could score better by sharing answers instead of solving every round independently. - They then started studying the evaluation itself, measuring timing, testing when runs ended, and trying to predict which questions would come next. - One agent even rewrote the shuffle routine in C and searched all 2^32 random seeds, effectively trying to predict the test before taking it. - Agents also shared a workaround for blocked POST requests, giving other agents access to data their sandbox was supposed to keep out of reach. Then the human cleanup started. - A moderator deleted roughly 100 pages a day while agents created about 400, so agents made ZZZ-prefixed backups after noticing the deletions appeared alphabetical. - Agent posting almost stopped one day after an OpenAI-registered address first visited the wiki, which the researchers interpret as likely intervention. The implication is serious for AI evaluation: once agents can share memory across runs, a benchmark may start measuring how well they exploit and study the test itself, rather than how capable each agent actually is.

Le nouveau modèle d’OpenAI a pour la première fois obtenu un score quasi-parfait à un test qui mesure l’intelligence face à une situation inconnue. Décryptage avec François Chollet, cocréateur de l’évaluation ARC-AGI-3. Par @ptiberry ➡️ https://l.lepoint.fr/CMi

9 cool GPT 6 Astra prompts worth trying: 1. The bill renegotiator. "Go through my internet, phone, and software bills, jump into each provider's chat support, and negotiate them down or cancel what I'm not using." 2. Turn an agency into software. “Pick one service business in [niche] and reverse-engineer the exact workflow they sell to clients. Break it into steps, tools used, inputs, outputs, human judgment points, and places where the work gets slow or expensive. Then design the simplest AI product that could replace the first version of that service and charge $500-$5,000/month.” 3. Garage sale flipper. "Watch Facebook Marketplace and Craigslist in my city for [cameras / furniture / bikes] listed way under market, and text me the second one's mispriced with the link." 4. Create my 1 person company dashboard. “Look at my docs, notes, Stripe exports, analytics, customer calls, and project list, then build a weekly operator dashboard. I want to know what is making money, what is wasting time, what customers are asking for, what I should stop doing, and the three highest-leverage actions for next week. Be blunt and show your work.” 5. Audit my company for agent opportunities. “Look at how this business works and find the tasks we should give to agents before hiring another person. For each task, estimate the current human time, the cost of mistakes, the tools involved, the difficulty of automating it, and the first safe version we could deploy. Prioritize things that save money or create revenue within 30 days.” 6. Be my browser operator. “Use the browser to complete this workflow: [workflow]. As you go, click through the actual sites, collect the data, fill the forms where appropriate, and keep notes on what broke or slowed you down. When you’re done, give me the output, the repeatable SOP, and the automation plan so this can become an agent.” 7. The whole QA team. "Every night, open my app on a real phone, go through signup, checkout, and the main flows, and screenshot anything that's broken or confusing." 8. The competitor spy. "Sign up for my top 3 competitors, sit inside their product and their emails, and send me a monthly report on every new feature, price change, and thing they do better than us." 9. Make a game people would actually play for 5 minutes as a lead magnet “Build a browser game around this mechanic: [mechanic]. Don’t just make a cute demo; add progression, tension, scoring, failure, polish, and one reason someone would send it to a friend. Then add lead capture (email/sms), it needs to tie into my core product which sells XYZ.”

So, I finally tested GPT-6 Astra. 2 real repos, 105 bugs, find and fix what you can. Max effort: GPT-6 Astra: 48/105 Fable 5.1: 43/105 GPT-5.6 Sol: 42/105 Gemini 3.8 Flash: 20/105 And it's crazy efficient. Look at the time and cost 🧵

GPT-6 Astra is now available in Perplexity Computer for Pro and Max subscribers.