dekhn

I disagree with your premise. Part of our job as scientists (thankfully no longer mine) is to reduce the irrelevant and incorrect noisy as early as possible. I have seen so many grad students get excited by a paper and put enormous effort into reproducing somethign that was a false or fake result.

To me, a single significant error of any kind brings the entire paper into question. If a less important figure contains an image duplication, that makes me wonder if I can trust any of the images.

The next example I can think of- I am not sure it qualifies. I read a paper where they deleted one gene at a time in yeast (it has 6000 genes) and determined whether the mutated yeast could live or not. For each gene where the yeast died, they added that to a list of "essential for life" genes. The paper concluded they had found some interesting proteins that should be studied. I read the paper and the first thing that sprang to mind, are any of these genes overlapping? Because we know (somebody already demonstrated in a lab) that genes do overlap (which is truly weird!)

I wrote a script and showed that every gene they reported as essential for life overlapped an already known gene that was essential for life. I wrote the authors, who never responded, but wrote a followup paper where they acknowledged they probably had a high false positive rate due to overlapping genes with known fatal effects. My guess is you'd say that either I used a model (existing literature) or I compared against the world, but realistically, what I did was trivially come up with a better explanation than the authors. T hat's what I want LLMs to do for me, and it seems like the direction LLMs are going will fulfill my desires.

I work full time on "lab in the loop" AI, so I'm pretty familiar with the need for real-world experiments. I am not proposing a fully autonomous scientist that could read an arbitrary paper and emit whether it's universally true without some verification method.

Also, to your statement: " Because by the time you are a practicing scientist, you've developed a feel for what constitutes a satisfying explanation."

I'm a practicing scientist (well, ex-scientist) and it seems like most "satisfying explanations" end up being wrong or incomplete simply because they seem so satisfying.

"Abstract The Paxos algorithm, when presented in plain English, is very simple."

And amyloid protein doesn't self-replicate. An instance of the misfolded form causes other folded proteins of the same type to transition to the misfolded form. It's a form of catalysis, not self-replication.

Pagerank was inspired by academic citation networks; it just turned it in a recursive matrix problem (of which there was some prior literature).