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Test-time scaling has two axes: running agents over longer timeframes (depth), and running a larger number of agents (breadth). Everybody knows about the first axis, but the second one is just as important when solving hard problems that require broad search.

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.

🤯 跑了 13h 的 GPT-6 Astra 地狱难度测试总结，应对专业系统性任务到底啥水平？ 5 项任务总得分：60/100（及格线），消耗 token：2.1 亿 1）使用行业软件建模，复原度 70% 2）建筑微电影质量 75% 3）可交付施工图完成度 40% 4）展示图/Diagram 质量 60% 5）使用生成式 AI 做风格化短片 55% 输入：单图+提示词 要求：传统流程+软件 运行时长：>12h 消耗 token：2.10 亿 交付结果（对应视频演示）： 1）Rhino 建筑模型 2）Grasshopper 帆面分格 3）离线空间漫游 4）BIM 模型 5）一套 CAD 图纸 6）2张 A1 建筑分析 7）2张 A1 项目展板 8）一段3min建筑短片（建模+渲染+剪辑） 9）一段2min以建筑为背景的悬疑短片（建模+AI视频生成） 10）点云修正 11）建筑资料研究 12）总体任务书 13）3 组独立曲面修正