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README.md

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+---
+license: cc-by-4.0
+pipeline_tag: other
+tags: [physical-ai, world-model, learned-simulator, promptable, on-device, in-browser, genie, oasis]
+---
+
+# physicalai-bmi/nano-world-model-multi
+
+A **promptable, multi-environment learned simulator**. ONE small fully-convolutional
+network predicts the next frame of a controllable scene from the last two frames, your
+action, **and an environment code**. Pick a world — arena, moon, ice, mud, downhill, wind — or type one, and the same
+weights simulate that world's physics. There is **no physics engine at run time**: the
+network *is* the simulator. It runs entirely **in the browser, in plain JavaScript**,
+live at [/research/world-model](https://physicalai-bmi.org/research/world-model).
+
+This is the Genie / Oasis idea — *a prompt selects the world* — at nano scale, released
+and runnable on the device in front of you. Companion to the single-environment
+[nano-world-model](https://huggingface.co/physicalai-bmi/nano-world-model).
+
+## What it is
+
+- **Input:** two stacked RGB frames (32×32) + a 2-axis action + a
+  6-way **environment one-hot**.
+- **Model:** 48,963 parameters — `cat(prev, cur, action×2, env-onehot×6) →
+  4 × conv 3×3 (SiLU) → residual + sigmoid`. Fully convolutional; the same weights render
+  every world.
+- **Training:** a multi-step **rollout loss** (predict K steps from its own predictions,
+  across all environments) so each world stays stable and playable when it drives itself.
+  Motion-weighted so the moving object dominates the static scene.
+
+## The worlds
+
+Each environment has distinct dynamics (thrust gain, friction, wall restitution, ambient
+gravity/wind) and a visual tint the network renders forward. The environment one-hot is
+the only thing that changes between them — the pixels of the seed frame are otherwise the
+same, so the network is genuinely simulating different physics *from the code*, not the image.
+
+## Honest metrics (held-out rollouts)
+
+- one-step MSE: **1.15e-04**
+- 30-step rollout MSE: **1.67e-03**
+
+Per-world coherence (min moving-object peak brightness over a 50-step self-driven rollout;
+near 1 = stays a sharp, localized blob rather than blurring away):
+
+| world | coherence min-peak |
+|---|---|
+| arena | 0.66 |
+| moon | 0.538 |
+| ice | 0.593 |
+| mud | 0.737 |
+| downhill | 0.541 |
+| wind | 0.583 |
+
+It is deliberately tiny and is an approximation, not a perfect simulator: over a long
+unbroken run the network can let the object soften or drift, because it is guessing every
+pixel from what it learned rather than solving equations. The frontier versions of this
+idea (Genie, Oasis, DIAMOND) are hundreds of millions of parameters and need a GPU; this
+one shows the same mechanism, released and runnable, on-device.
+
+## Files
+
+- `model.web.json` — portable weights + config (envs, tints, aliases) for the browser runtime.
+- `model.safetensors` — the same weights.
+- `metrics.json` — the numbers above.
+
+Released CC-BY-4.0 by the Institute for Physical AI @ BMI.