https://hackerpaper.kaipfister.de/p/2509.13805/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=45289014urn:hackerpapers:mention:2509.13805:45289014Thu, 18 Sep 2025 12:51:57 GMT<p>The point has nothing to do with speed, but with expressive power / what is achievable and learnable, in principle. Again, if you accept that a computer can in principle run a program that qualifies as AGI, then all I&#x27;m saying is that an LLM with memory augmentation can in principle be trained to do this as well because their computation power is formally equivalent. And coincidentally, a new paper being discussed on HN is a good example addressing your concern about existing models learning and developing novel things. Here&#x27;s a GPT model that learned physics just by training on a data…</p><p><a href="https://hackerpaper.kaipfister.de/p/2509.13805/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LGhttps://news.ycombinator.com/item?id=45284766urn:hackerpapers:submission:2509.13805:45284766Thu, 18 Sep 2025 03:06:08 GMT<p>Towards a Physics Foundation Model</p><p></p><p>117 points; 30 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/2509.13805/">Paper and source history</a>. A submission is not an endorsement.</p>cs.LG