Don’t get me wrong, I love standard LLMs for boilerplate and quick scripts. But at the end of the day, autoregressive models are just highly educated guessers playing a massive game of autocomplete. They don’t actually reason about the state of the system they are building. I’ve been going down the rabbit hole of Yann LeCun’s Energy-Based Models (EBMs) and how neuro-symbolic logic is making a comeback. Instead of just spitting out tokens left-to-right, this architecture treats code generation like a constraint satisfaction problem. It evaluates the entire code block at once and runs an optimization loop to minimize the “energy” (meaning logical errors and unverified states) until the output is mathematically proven to work. I’ve seen a few early examples of a Coding AI adopting this exact EBM approach lately, moving away from pure statistical guessing toward actual verifiable logic. Honestly, it feels like the necessary next step if we ever want AI to write avionics or medical infrastructure without a human essentially rewriting it anyway. Do you guys think the industry is finally hitting the ceiling with the “just add more parameters to the transformer” approach? submitted by /u/datboifranco
Originally posted by u/datboifranco on r/ArtificialInteligence