Idk, but maybe not necessarily, we observe discrete tokens but the language states themselves can live in a continuous world.

Generative models that assume the underlying distribution is continuous, for example, flow matching and common diffusion models.

Orbis shows that the objective matters.
Continuous modeling yields more stable and generalizable world models, yet true probabilistic coverage remains a challenge.

Immensely grateful to my co-authors @arianmousakhan.bsky.social, Sudhanshu Mittal, and Silvio Galesso, and to @thomasbrox.bsky.social

Under the hood 🧠

Orbis uses a hybrid tokenizer with semantic + detail tokens that work in both continuous and discrete spaces.
The world model then predicts the next frame by gradually denoising or unmasking it, using past frames as context.

Realistic and Diverse Rollouts 4/4

Realistic and Diverse Rollouts 3/4

Realistic and Diverse Rollouts 2/4

Realistic and Diverse Rollouts 1/4

While other models drift or blur on turns, Orbis stays on track — generating realistic, stable futures beyond the training horizon.

On our curated nuPlan-turns dataset, Orbis achieves better FVD, precision, and recall, capturing both visual and dynamics realism.

We ask how continuous vs. discrete models and their tokenizers shape long-horizon behavior.

Findings:
Continuous models (Flow Matching) are
• Far less brittle to design choices
• Produce realistic, stable rollouts up to 20s
• And generalize better to unseen driving conditions

Continuous > Discrete

Driving world models look good for a few frames, then they drift, blur, or freeze, especially when a turn or complex scene appears. These failures reveal a deeper issue: models aren’t capturing real dynamics. We introduce new metrics to measure such breakdowns.

The question raised here is whether this approach is a generalist or a specialist that cannot transcend to the G-foundation state.

I think HRM is quite great too. I would say they contributed the main idea (deep supervision) behind TRM.

Transformers do not need to have something like "gradient descent" as an emergent property when it is kind of baked into it.