AI 智能体在模型开发中承担更多工作，但人类仍做决策

A research team documented how humans and AI agents worked together to build a new AI model. The findings challenge some expectations about how independently agents can work. When AI agents help build new AI models, who makes the decisions? A team involving researchers from China's Fudan University studied its own project to find out. It analyzed more than 700 task logs from 56 participants, along with logs from the agents they used. The project centered on developing an agentic language model called Atria Dawn Preview , built on a mixture-of-experts architecture with 744 billion parameters and designed for research and engineering tasks. The model was trained through a pipeline that ties each task to a real execution environment. It calls tools, generates intermediate results, and gets checked against external signals like tests, metrics, or source evidence. The team says it leads on five of 16 benchmarks, including web search and cybersecurity, though it doesn't hold an overall edge over competitors. AI was used in 96.5 percent of the tasks reviewed. Over the course of the project, participants handed off more and more to agents. The median ratio of agent actions to human inputs rose from 11 to 28.5 over four weeks. The team cautions against reading this as growing autonomy. Each human decision led to more agent steps, which didn't mean the agents were making more decisions themselves. Participants were also asked whether they could have completed their share of a task without AI, at the same scope and quality. Of 455 completed AI-assisted tasks, 151 were rated infeasible without AI, roughly a third. These tasks were spread across 27 of the 56 participants, so they didn't come from just a handful of power users. AI didn't speed up existing work in these cases. It made work possible that would never have been started otherwise. For methods and parameters, the most common pattern was "AI proposes, human selects" at 55.4 percent. Overall, humans made 85.5 percent of decisions about methods and parameters, while AI made just 9.2 percent. Humans made the final decision on goals and scope in 93.4 percent of cases. AI's share of proposals ranged from 17 to 55 percent depending on the decision type. Its share of final decisions stayed in the single digits. Who proposed the options varied widely, but humans consistently made most of the final choices. Even among the 151 tasks rated infeasible without AI, humans chose the goal 95.4 percent of the time. The same pattern shows up when things go wrong. Of 588 tasks with a recorded difficulty, 76 percent moved forward through human intervention, and in 23 percent the agent solved the problem on its own. Human help almost always came in the form of information, either by adding context or clarifying requirements (35.2 percent) or by diagnosing issues and switching methods (34.7 percent). Humans rarely did the work themselves. Partial edits accounted for 3.2 percent of cases, and full takeovers just 0.7 percent. When AI outputs needed revision, the AI handled the changes itself 75.4 percent of the time after receiving human feedback. Human judgment, rather than execution, was the bottleneck. The team describes three phases in AI's role, from a subject of research to a tool for individual tasks and now a project partner. In that current role, AI drafts and adjusts plans within goals set by humans. A speculative fourth phase would involve recursive self-improvement, with stronger models producing stronger successors. The authors say a model can improve at its training tasks without getting better at developing its successor. How AI could propose varied research directions and assess their value before results are available remains an open question.