I have not read this before, thanks. Reminds me a lot of Normal Computings extended mind models. I think these are good ideas worth testing, and there are many others within the same vein. My intuition suggests that any idea that pursues a gradual increase in global information prior to decoding is a worthwhile experiment, whether through your method or similar (doesn’t necessarily have to be diffusion on embeddings).
Aesthetically I just don’t like that transformers have an information collapse on each token and don’t allow backtracking (without significant effort in a custom sampler). In my ideal world we could completely reconstruct prose from embeddings and thus simply autoregress in latent space. I think Yann Lecun has discussed this with JEPA as well.
I originally had my thought from a frequency autoregression experiment I had, where I used a causal transformer on the frequency domain of images (to sort of replicate diffusion). This gradually adds information globally to all pixels due to the nature of the ifft, yet still has an autoregressive backend.
You might be interested in a small “hybrid LLM” proposal I wrote for using diffusion on embeddings for then decoding/sampling.
I have not read this before, thanks. Reminds me a lot of Normal Computings extended mind models. I think these are good ideas worth testing, and there are many others within the same vein. My intuition suggests that any idea that pursues a gradual increase in global information prior to decoding is a worthwhile experiment, whether through your method or similar (doesn’t necessarily have to be diffusion on embeddings).
Aesthetically I just don’t like that transformers have an information collapse on each token and don’t allow backtracking (without significant effort in a custom sampler). In my ideal world we could completely reconstruct prose from embeddings and thus simply autoregress in latent space. I think Yann Lecun has discussed this with JEPA as well.
I originally had my thought from a frequency autoregression experiment I had, where I used a causal transformer on the frequency domain of images (to sort of replicate diffusion). This gradually adds information globally to all pixels due to the nature of the ifft, yet still has an autoregressive backend.