AI终于能干活干到底了
现在的AI聊天很聪明，但一让它干点正经事——比如整理一堆文件、跑个数据分析、再写个报告——它经常半路就懵了，或者忘了自己干到哪。这篇搞了个叫Apodex 1.1的系统，专门解决这个问题：它把AI的能力拆成两路——一路是让它能真正操作文件、搜索、跑代码，另一路是训练它自己把长任务拆成小步、分给多个AI同时干、再把结果合起来，中间还能记住进度、出错了自己恢复。35B参数的小模型版本就能在金融、科研、编程等专业任务上追上甚至超过那些大得多的模型。它不是你明天就能装到自己电脑上用的东西，但它指向一个方向：AI从“能聊”变成“能干完活”。
📄 原文摘要(英文)
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this working capability: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. Environment Scaling expands the diversity and verifiability of executable file, search, and code environments, while Agentic Coordination Scaling trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a Heavy-Duty Solver for ambitious, long-running tasks.