[D] Is the move toward Energy-Based Models for reasoning a viable exit from the “hallucination” trap of LLMs?

I’ve been stuck on the recent back-and-forth between Yann LeCun and Demis Hassabis, especially the part about whether LLMs are just “approximate Turing Machines” or a fundamental dead end for true reasoning. It’s pretty wild to see LeCun finally putting his money where his mouth is by chairing the board at Logical Intelligence, which seems to be moving away from the autoregressive paradigm entirely.

They’re building an architecture called Kona that’s rooted in Energy-Based Models. The idea of reasoning via energy minimization instead of next-token prediction is technically interesting because it treats a solution like a physical system seeking equilibrium rather than just a string of guessed words. I was reading this Wired piece about the shift they’re making, and it really highlights the tension between “System 1” generation and “System 2” optimization.

If Kona can actually enforce hard logical constraints through these EBMs, it might finally solve the reliability problem, but I’m still skeptical about the inference-time cost and the scaling laws involved. We all know why autoregressive models won – they are incredibly easy to scale and train. Shifting back to an optimization-first architecture like what Logical Intelligence is doing feels like a high-stakes bet on the “physics” of reasoning over the “fluency” of language.

Basically, are we ever going to see Energy-Based Models hit the mainstream, or is the ‘scale-everything-autoregressive’ train moving too fast for anything like Kona to catch up?