Abridge

Abridge AI

Abridge AI

Abridge AI

Abridge AI

Abridge AI

Abridge AI

Abridge AI

Abridge AI

Abridge AI

Abridge AI

OpenAI claims a general-purpose reasoning model found a counterexample to Erdos's unit-distance bound [D]

OpenAI posted a math result today claiming that one of its general-purpose reasoning models found a construction disproving the conjectured n^{1+O(1/log log n)} upper bound in Erdős’s planar unit-distance problem. Announcement: https://openai.com/index/model-disproves-discrete-geometry-conjecture/ Proof PDF: https://cdn.openai.com/pdf/74c24085-19b0-4534-9c90-465b8e29ad73/unit-distance-proof.pdf Abridged reasoning writeup: https://cdn.openai.com/pdf/1625eff6-5ac1-40d8-b1db-5d5cf925de8b/unit-distance-cot.pdf The mathematical claim, as I understand it, is that there are finite planar point sets with more than n^{1+δ} unit distances for some fixed δ > 0 and infinitely many n. That would rule out the expected near-linear upper bound, though it does not determine the true asymptotic growth rate. What seems especially relevant for this subreddit is the process claim: OpenAI says the solution was produced by a general-purpose reasoning model, then checked by an AI grading pipeline and reviewed/reworked by mathematicians. The proof PDF also includes the original prompt given to the model, but not the full experimental details: no model name, sampling setup, number of attempts, compute budget, hidden system prompt, or full grading pipeline. Curious how people here read this as an ML result. Is this best viewed as evidence of frontier models doing genuine autonomous research, or as a cherry-picked but still important sample from a large search process? What kind of disclosure would you want before treating this as a reproducible AI-for-math milestone? submitted by /u/NutInBobby [link] [comments]

ML lead vs PM on eval-methodology layer independence. who's actually right here? [D]

got into an argument with our ML lead at 11pm yesterday about an eval methodology a PM had built off a framework she learned at an AI PM cohort. shes claiming a layered defense framework, hes saying the layers are statistically conditioned and her independence claim is wrong. they both have a point. the framework as taught at the cohort (it was Product Faculty's, fwiw) is genuinely useful for non-eng PMs. it forces explicit thinking about behavioral checks vs adversarial probes vs traditional metrics. but the way it's been taught in the abridged form makes the layers sound independent when they statistically arent. for ML/AI engineers here who've worked with non-eng PMs on production eval. how do you handle the gap between the simplified eval frameworks PMs learn and the actual statistical interactions in production? specifically interested in how you've negotiated the conversation with a PM who's ""done the cohort"" and shows up with a framework that's solid in its public form but has subtle issues in its statistical foundations. submitted by /u/Critical_Builder_902 [link] [comments]

A TTRPG I wrote months ago in Word, can now have a professional looking rulebook within the week...

I wrote this TTRPG called "QuestR," it's one of my biggest passion projects but it's only lived as a word doc till now, cause I don't have the 1000s of dollars required to get the entire thing professionally illustrated and typeset, but with the new image generator I can literally do it in seconds providing nothing but the info in the original word doc. The new image generation isn't perfect, but fixing minor typos here and there in photoshop is NOTHING compared to labor of mocking up an entire page of my own. The new generator is insane. submitted by /u/BodaciousMonk [link] [comments]