Columbia CS Professor: Why LLMs Can’t Discover New Science
Vashan Vishal argues that current LLMs are not AGI because they only navigate existing 'Bayesian manifolds' derived from training data, rather than creating new scientific paradigms.summaryHe introduces a formal model where LLMs perform Bayesian inference on a compressed matrix of token distributions, explaining phenomena like in-context learning and the effectiveness of chain-of-thought through entropy resummaryDefinition of AGI: AGI is defined as the ability to create new science, math, or paradigms (like Einstein's relativity) that go beyond the training distribution, whereas current LLMs only refine and navigate existing knotakeaway














