[bjornsing]

> data-to-paper is a framework for systematically navigating the power of AI to perform complete end-to-end scientific research, starting from raw data and concluding with comprehensive, transparent, and human-verifiable scientific papers (example).

Even if this thing works I wouldn’t call it “end-to-end scientific research”. IMHO the most challenging and interesting part of scientific research is coming up with a hypothesis and designing an experiment to test it. Data analysis and paper writing is just a small part of the end-to-end process.

[rlt]

The very next paragraph:

> Towards this goal, data-to-paper systematically guides interacting LLM and rule-based agents through the conventional scientific path, from annotated data, through creating research hypotheses, conducting literature search, writing and debugging data analysis code, interpreting the results, and ultimately the step-by-step writing of a complete research paper.

[bjornsing]

> from annotated data, through creating research hypotheses

Then it’s all just wrong, automated p-hacking. You’re supposed to start with the hypothesis, not generate it from the data you’re about to publish.

[YeGoblynQueenne]

More to the point you're supposed to start with an observation that your current theory can't explain. Then you make a hypothesis that tries to explain the observation and collect more observations to try and refute your hypothesis; if you're a good falsificationist, that is. That doesn't seem to be the process described above. Like you say it's just a pipeline from data to paper, great for writing papers, but not much for science.

But I guess these days in many fields of science and in popular parlance "data" has become synonymous with "observation" and "writing papers" with "research", so.