diyer22

MathArena uses newly released competition sets and evaluates models close to the event. They also mark models released after the competition date as potential contamination.

On Feb 6, the just-concluded AIME 2026 I, Step 3.5 Flash take first place. Step 3.5 Flash was released on Feb 1, making cheating impossible.

A record of getting burned by a “sloppy, low-quality, and irresponsible” paper from Big Tech.

I believe DDN is capable of handling TTS (text-to-speech) tasks, because with the text condition, the generation space is significantly reduced.

And it's recommended to combine it with an autoregressive model (GPT) for more powerful modeling capabilities.

During neural network training, the ground truth (GT) must be known to compute the loss.

In DDN, the GT is only used to calculate the loss and guide sampling; it never becomes an input to the model.

Thanks for the idea, but DDN and flow can’t be flipped into each other that easily.

1. DDN doesn’t need to be invertible. 2. Its latent is discrete, not continuous. 3. As far as I know, flow keeps input and output the same size so it can compute log|detJ|. DDN’s latent is 1-D and discrete, so that condition fails. 4. To me, “hierarchical many-shot generation + split-and-prune” is simpler and more general than “invertible design + log|detJ|.” 5. Your design seems to have abandoned the characteristics of DDN. (ZSCG, 1D tree latent, lossy compression)

The two designs start from different premises and are built differently. Your proposal would change so much that whatever came out wouldn’t be DDN any more.

It does seem that way — we’re both trying to overcome the limitations imposed by LLM tokenization to achieve a truly end-to-end model.

And, their work is far more polished; I’ve only put together a quick GPT+DDN proof-of-concept.

Thank you for sharing.

Yes, there is a transform that make final size of stem features remains unchanged

Even in the earliest stages of the DDN concept, we had already decided to pass features down to the next layer.

I never even ran an ablation that disabled the stem features; I assume the network would still train without them, but since the previous layer has already computed the features, it would be wasteful not to reuse them. Retaining the stem features also lets DDN adopt the more efficient single-shot-generator architecture.

Another deeper reason is that, unlike diffusion models, DDN does not need the Markov-chain property between adjacent layers.