做科研自动化的开发者会看到智能体在数学/物理问题上的真实表现——不是万能 oracle，而是高效的科研劳动力，值得了解其能力边界。
一位研究者分享了使用 Codex 处理开放数学/物理问题的经验，发现并行智能体在需要大量顺序推理的问题上效果有限。真正的超人类能力体现在速度、知识量和可复制性上，而非瞬间解决难题。前沿物理问题比老旧的数学问题更容易被智能体处理，但研究品味仍是瓶颈。智能体擅长在给定方向后推进，但选择问题和判断方向仍需人类。
i've been running Codex for ~8-24h per open math/p…
i've been running Codex for ~8-24h per open math/physics research problem. few thoughts:
parallel agents don't seem to scale that cleanly for a lot of problems. many of these are just extremely sequential. you don't really get to "spawn 50 agents and solve it from nowhere." it's more like: tiny move, check, reframe, tiny move, dead end, try again. hours/days of serial cognition, which honestly rhymes with how these fields move over decades.
this updates me a bit against the sci-fi picture of "superhuman math/physics intelligence" as some alien oracle that instantly sees the proof / theory.
the actual superhuman-ness is more mundane and maybe more important: the agent has absorbed a huge prior, can read long papers basically instantly, can think/write at >50 tok/s, and you can clone it across dozens of problems. speed + knowledge volume + multiplicability. that's the superpower.
also: frontier physics seems much more tractable for these agents than decade-old open math problems. for some physics directions, ~8h is enough to get something paper-shaped and nontrivial.
big caveat tho: research taste is still missing. the agent is a pretty good problem-solver, but not yet a top-tier problem-picker. it can push hard once the direction is chosen, but you probably still want a human with taste choosing the problem / framing / bet.
current model: agents are becoming very strong research labor, but the bottleneck shifts upward into taste, problem selection, and knowing which hill is worth climbing.