Comment by VoidWhisperer 4 days ago
I don't have a super deep understanding of the underlying algorithms involved, but going off the demo and that page, is this mainly a model for image related tasks, or could it also be trained to do things like what GPT/Claude/etc does (chat conversations)?
This is what I find most impressive, that it's a natural hierarchial method which seems so general, yet is actually quite competitive. I feel like the machine learning community has been looking for that for a long time. Non-generative uses (like hierarchial embeddings, maybe? Making Dewey's decimal like embeddings for anything!) are even more exciting.
Exactly! The paragraph on Efficient Data Compression Capability in the original paper also highlights:
> To our knowledge, Taiji-DDN is the first generative model capable of directly transforming data into a semantically meaningful binary string which represents a leaf node on a balanced binary tree.
This property excites me just as much.
This sounds a bit like H-Net [1] or Byte Latent Transformer [2].
The posts mentions that: https://github.com/Discrete-Distribution-Networks/Discrete-D...
Yes, it's absolutely possible—just like how diffusion LLMs work, we can do the same with DDN LLMs.
I made an initial attempt to combine [DDN with GPT](https://github.com/Discrete-Distribution-Networks/Discrete-D...), aiming to remove tokenizers and let LLMs directly model binary strings. In each forward pass, the model adaptively adjusts the byte length of generated content based on generation difficulty (naturally supporting speculative sampling).