[encypherai]

Thanks for the detailed explanation of autoregression and its complexities. The distinction between architecture and loss function is crucial, and you're correct that fine-tuning effectively alters the behavior even within a sequential generation framework. Your "An/A" example provides compelling evidence of incentivized short-range planning which is a significant point often overlooked in discussions about LLMs simply predicting the next word.

It’s interesting to consider how architectures fundamentally different from autoregression might address this limitation more directly. While autoregressive models are incentivized towards a limited form of planning, they remain inherently constrained by sequential processing. Text diffusion approaches, for example, operate on a different principle, generating text from noise through iterative refinement, which could potentially allow for broader contextual dependencies to be established concurrently rather than sequentially. Are there specific architectural or training challenges you've identified in moving beyond autoregression that are proving particularly difficult to overcome?