Over the past year, my lab has been working on fleshing out theory + applications of the Platonic Representation Hypothesis.

Today I want to share two new works on this topic:

Eliciting higher alignment: arxiv.org/abs/2510.02425
Unpaired learning of unified reps: arxiv.org/abs/2510.08492

1/9

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.

Our work Orbis goes to #NeurIPS2025!

A continuous autoregressive driving world model that outperforms Cosmos, Vista, and GEM with far less compute.

469M parameters
Trained on ~280h of driving videos

📄 arxiv.org/pdf/2507.13162
🎬 lmb-freiburg.github.io/orbis.github...
💻 github.com/lmb-freiburg...

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.

The TRM works because it has an optimization algorithm as an inductive bias to find the answer. Can't say anything about this work but brilliant.

We should normalize having the ‘Ideas That Failed’ section. It would save enormous amounts of compute and time otherwise spent rediscovering stuff that doesn’t work.

I stumbled on @eugenevinitsky.bsky.social 's blog and his "Personal Rules of Productive Research" is very good. I now do a lot of things in the post, & wish I had done them when I was younger.

I share my "mini-paper" w ppl I hope will be co-authors.

www.eugenevinitsky.com/posts/person...

Just had an idea

My major realization of the past year of teaching is that a lot is forgiven if students believe you genuinely care about them and the topic

Possible challenge: getting a model of {X,Y,Z,...} that is much better than independent models of each individual modality {X}, {Y}, {Z}, ... i.e. where the whole is greater than the sum of the parts.

I also really hope that the LAM from V1 is still there!

Inspiring! Genie incentives generative models to learn actionable latent states by enforcing a latent action model. Action spaces and actionable states are entangled, so more causal WMs. However, I was wondering why would you call the “counterfactuals” counterfactual? Sounds more like interventional

Nice! There was some skepticism around diffusion models representation learning capacity as they do not optimize for an explicit abstraction loss as other SSL models.

I guess the work would benefit a lot from a comparison with SODA, what do you think?

arxiv.org/abs/2311.17901

I'm excited about scaling up robot learning! We’ve been scaling up data gen with RL in realistic sims generated from crowdsourced videos. Enables data collection far more cheaply than real world teleop. Importantly, data becomes *cheaper* with more environments and transfers to real robots! 🧵 (1/N)