Having read the article, it seems like an interesting experiment. With the current state of LLMs, this is extremely unlikely to produce useful research, but mos

Having read the article, it seems like an interesting experiment. With the current state of LLMs, this is extremely unlikely to produce useful research, but most of the limitations people have been commenting about are will progressively get better. The authors' credibility is a bit hurt when the first "limitation" they mention is "our system doesn't do page layouts perfectly". Come on, guys. What is weird to me is this: > The AI Scientist occasionally makes critical errors when writing and evaluating results. For example, it struggles to compare the magnitude of two numbers, which is a known pathology with LLMs. To partially address this, we make sure all experimental results are reproducible, storing all files that are executed. I'm not sure why you would run your evaluation step without giving your LLM access to function calling. It seems within reach to first have the LLM output a set of statements-to-be-verified (eg, "does X increase when Y increases?") and then use their code-generation/execution step to perform those comparisons. And then the incomprehensible statement for me here is that they allow the model access to its own runtime environment so it can edit its own code? The paper is 185 pages and only has one paragraph on safety. This screams "viral marketing piece" rather than "serious research". And finally: > The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference... Oh wow, please tell more? > ... as judged by our automated reviewer. Ah. Nevermind