rsfern

I also found your post lacked coherence, but I agree GP’s feedback is delivered a bit harshly

- the way you jump right in is kind of off topic with tons of details, your connecting sentence about Kepler would have been better framing context to lead with (but it also needs revision because it reads like you’re elbowing into the conversation)

- HN is a general (technical) audience, your opening statement and the rest of your post assumes a lot of specialized knowledge. It needs some scaffolding and some distillation for a broad audience to understand what’s important about what you did

- that opening sentence isn’t grammatical? Your solution is faster than some quantum theory of gravity? Or maybe you missed a word and mean it’s more accurate than QED? And it’s a theory of gravity that predicts orbital resonance?

- you’re mixing conceptual and implementation details with the effect of information overload. Is the interesting part the theory of gravitation or the fact that you used Rust and WASM? I would lead with the scientific part, then add a separate sentence on the computational tools you used so they both get the focus they deserve

- the rest of your post is long and kind of alternates between what looks like pasted autogenerated link summaries, jargon-filled descriptions of the research, and some descriptions of visualization tools that don’t clearly connect (to someone without expertise in this field) to the rest of the post

- there’s a flood of crap science being cranked out with agents. Maybe your project is not one of these, but you’re fighting an uphill battle against everyone’s heuristics to filter out the noise. If you want to be engaging you have to distill, clarify, and adapt to your audience

Counterpoint, I’ve been one those surface level emacs users for years and coding agents have been super helpful for me to learn elisp and some emacs internals by hacking on some org mode and elfeed extensions

Doesn’t this mess up the context though? I use ruff and my agents get confused if I edit and run ruff out of band. How do people deal with that? I’m always having to remind agents to re-read files because they’ve changed

How does one develop strong judgement without strong technical knowledge?

I think this is a difficult counterfactual to assess, we didn’t develop clock accurate enough to notice the time dilation until 50 plus years after the theory of relativity was published.

Still, how long would it take someone to work it out empirically enough to have confidence that there wasn’t just some bug somewhere? Maybe they would mothball the GPS project for not working before figuring it out? It’s hard to know, 50 years in an alternate timeline allows for a lot of domino effect

It can still be a good long term investment for society even if individual researchers are primarily motivated more by understanding than application. Arguably it’s the best approach we have because reliably predicting which ideas will be fruitful is super hard

I don’t work in drug discovery, but isn’t this thought experiment skipping a lot of steps? My impression is AI is accelerating a lot of the preclinical screening and design work, but that’s pretty far from establishing “tremendously improved outcomes for patients” isn’t it? That’s still really difficult to verify, are people getting real acceleration of this part of the work with AI?

> if openai had provided really readable proofs with utmost care but still dropped 400 at once, would there have been less outrage?

Less criticism, yes

There’s always going to be outraged people, but outrage isn’t the word I would choose to describe the positions of the mathematicians I’ve read on this topic, including TFA. There’s a lot of optimism mixed with frustration that something important is missing

I think you’re out of touch with the discourse in the mathematical community, it’s quite relevant because clear communication is an important aspect of intelligence

Proofs can be unreadable for more than one reason. Are these ones unreadable because the math is super advanced or because current agents suck at clear writing? Maybe a bit of both?

The progress is clearly not uniform though, it’s shaped by how amenable things are to collecting data and verifying computation. I think writing clear and cogent prose is harder to quantify than formally verifiable logic, and this is why progress in coherent communication has been slower than raw problem solving ability

Maybe this is just a matter of what model developers choose to invest training resources in, but I don’t think it’s inevitable unless clarity is made a higher priority

Are they fully “doing math” in an aligned way though? They are finding mathematical proofs, which is important, but contextualizing results in the prior literature and clearly communicating the approach and implications is just as much part of mathematical research.

You might argue that these aspects of math are less important in the new AI accelerated math world, because agents will inevitably be smarter than humans, but I think clear framing and communication is even more important than before because with this technology we can choose to augment our intelligence instead of defer it

Proof of what? There is a sign error in one of the proofs, OpenAI acknowledged it and withdrew three papers (two relied on the result).

I agree LLM review is also fallible (as is human review) but the interesting part to me is that finding this sign error before publication should have been table stakes for OpenAI, it’s their own model that found the sign error.

I’m curious what was in the original prompt and what was in the prompt that led to finding the sign error, I think it matters a lot for understanding the dynamics here