World models share one architecture — the tokenizer is where methods diverge
abursuc · x · 2026-09-17
In an SSAD 2026 lecture, the author breaks down the common architecture of modern world models: a tokenizer that compresses state into representations, and a world model module that predicts next states. The key design divergence is what the tokenizer encodes: frozen visual autoencoders, frozen representation encoders, or joint encoder+predictor training without a decoder — choices that explain the differences between approaches like JEPA-style and diffusion world models.
Related event: SSAD 2026 Talks Map World Model Architecture Choices(4 posts)→
More from Research
- Cisco's 3B Model Matches GPT-5.5 on Bug Localization (0.223) With Far Fewer False Positives — shashib · 2026-09-17
- Parallel Decoding Distillation pushes generation efficiency for autonomous driving world models — abursuc · 2026-09-17
- NVIDIA's SpatialClaw uses code as action interface, beats prior agent by 11.2 points on 20 benchmarks — CMHungSteven · 2026-09-17
- World model interactivity: handling mixed-frequency state updates at 10Hz — abursuc · 2026-09-17
- Researcher: AI math 'darlings' long relied on fake baselines, math lacks empirical tradition — RexDouglass · 2026-09-17
- CoLLAs 2026 keynote: Is continual learning trapped in obsolete abstractions? — apsarathchandar · 2026-09-17