What _non-autoregressive_ means hereIn this channel's material, non-autoregressive describes a model that produces output without sequences of tokens — the defining contrast with an LLM, which generates one token after another. It is used specifically to characterize Energy-Based Models (EBMs). [^c10ebe98-0850-5dfe-8c53-b988c268c606@11]How the term is usedThe clearest usage comes from the navigation analogy in The AI Model Built for What LLMs Can't Do: an autoregressive LLM driving a car picks one direction at a time, may hallucinate a wrong turn, and cannot turn back even when it sees the hole in the road ahead. An EBM, by contrast, is said to have a first view all the time — so when it spots a problem, it can choose a different route. [^c10ebe98-0850-5dfe-8c53-b988c268c606@22]There are no sequences of tokens and that's what makes it fundamentally different.That ability to reassess rather than commit token-by-token is presented as the core of the EBM's non-autoregressive nature, and the reason it suits deterministic, verifiable AI for mission-critical tasks like code generation and chip design. [^c10ebe98-0850-5dfe-8c53-b988c268c606@126]- Non-autoregressive = no sequences of tokens during generation. - It is the property that makes EBMs fundamentally different from LLMs in this material. - The payoff: an EBM keeps a first view all the time and can change course, while an autoregressive LLM cannot turn back. - The channel ties it to verifiable, deterministic AI for software and hardware correctness.[^c10ebe98-0850-5dfe-8c53-b988c268c606@11]: The AI Model Built for What LLMs Can't Do, from 0:11. [^c10ebe98-0850-5dfe-8c53-b988c268c606@22]: The AI Model Built for What LLMs Can't Do, from 0:22. [^c10ebe98-0850-5dfe-8c53-b988c268c606@126]: The AI Model Built for What LLMs Can't Do, from 2:06.