AI 生成研究想法的速度已超过研究者验证速度，ML 社区讨论“vibe-research”瓶颈

AI generates research ideas faster than I can test them. Anyone else? [R]

AI 导读

一位 ML 研究者指出，AI 让头脑风暴研究方向和生成 smoke run 实现变得极快，但实验验证仍是瓶颈：一次让 AI 审阅 30 篇论文的讨论就衍生出数十个可行方向，配合 coding agent 理论上可并行探索，研究者却仍需管理实验、启动训练、评估结果并判断下一步。该研究者称想法数量已多到难以跟进，询问他人是否有管理这种“vibe-research”节奏的日常实践。

正文

I've been using AI extensively for ML research, and I've noticed a bottleneck: brainstorming research directions and generating smoke run implementations has become incredibly fast, but actually testing those ideas hasn't.

A single AI discussion (eg asking it to review 30 papers I had in my reading list, audit them, check for ideas related to the status of my project) branched into dozens of promising approaches, and with coding agents, exploring them in parallel is in principle feasible. But as a researcher, I still have to manage experiments, launch training runs, evaluate results, and decide what to try next.

And, importantly, to understand what's gong on.

The result is an overwhelming number of potentially interesting ideas that I simply can't keep up with, risking to not really knowing what should be the smartest "next step".

I'm wondering if others are experiencing the same thing. Have you found good ways to manage this, or best daily practices that help in this era of "vibe-research"?

submitted by /u/Amazing-Coat5160
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