| --- |
| title: Cairn — Leave, Come Back, Same World |
| emoji: 🗿 |
| colorFrom: blue |
| colorTo: gray |
| sdk: static |
| pinned: false |
| license: mit |
| tags: |
| - world-models |
| - video-generation |
| - long-term-consistency |
| - object-permanence |
| short_description: Leave, come back, same world — and edit it |
| --- |
| |
| # Cairn — leave, come back, same world |
|
|
| Video world models forget. Turn the camera away from a chair for a few seconds and turn |
| back, and it is a different chair, somewhere else, or gone. The usual fixes give the model |
| *more implicit memory* — a longer context window, or a compressed latent. Both decay with |
| how long you looked away. |
|
|
| **Cairn takes the world out of the weights.** Objects live in an explicit external ledger — |
| persistent id, pose, appearance, provenance — written by perception on the model's *own |
| generated frames*, and read back to **coerce** generation when the camera returns. A table |
| lookup costs the same whether you looked away for 4 frames or 400. |
|
|
| Over 5 seeds, at 128 frames away, every baseline returns a broken world **0%** of the time |
| and Cairn **100%**, with a flat 3.6 cm position error. |
|
|
| ## What this Space does |
|
|
| Three things you can check yourself: |
|
|
| 1. **Leave & return** — drive the camera away from an object for *t* frames and come back. |
| Cairn OFF and Cairn ON generate from the same scene, the same trajectory and the same |
| seed, so the only difference is the memory. |
| 2. **Edit the world** — issue `move` / `remove` / `recolour` on an object **while it is off |
| screen**, turn back, and see whether the instruction stuck. The baselines cannot express |
| the command at all — there is no row to write to. |
| 3. **The ledger** — the actual table, its transaction log, and a rewind to any earlier frame. |
|
|
| ## How it runs |
|
|
| Entirely **in your browser**, via [Pyodide](https://pyodide.org) — no server, no GPU, no |
| account, nothing uploaded. The first load fetches Python, numpy, scipy and the `cairn` |
| wheel (~40 MB, cached afterwards); each run then takes a few seconds. |
|
|
| Pyodide has no ffmpeg, so instead of video you get a **filmstrip**: the opening shot, the |
| last frame before the camera leaves, a frame from while it is away, and the frames after it |
| comes back. For this claim a strip is arguably the better medium — both halves of the |
| comparison sit in one glance. |
|
|
| *Not using Gradio here on purpose: gradio-lite imports `gradio` before installing the page's |
| requirements, and gradio 5.x currently cannot be resolved against `huggingface-hub` 1.x |
| inside Pyodide. Driving Pyodide directly fixes the ordering and drops gradio's dependency |
| stack from the download.* |
|
|
| ## What you are watching |
|
|
| A **surrogate generator** that reproduces how autoregressive video drifts (random walk + |
| prior pull + salience decay) — not a real video backbone. That is a deliberate trade: exact |
| ground truth, and a benchmark that runs on a laptop in three minutes, in exchange for not |
| being LTX-Video. The same `cairn` library wraps a real diffusers video pipeline in one line: |
|
|
| ```python |
| cairn = CairnPipeline.from_pipeline(pipe) |
| ``` |
|
|
| Everything scientific — the generator, perception, the ledger, the forcing path, the |
| metrics — is the same code the benchmark measures, installed here from the same wheel. |
|
|
| ## Links |
|
|
| - **Code, full benchmark, ablation and honest limitations** — <https://github.com/NagaYu/cairn> |
| - **Dataset** — <https://huggingface.co/datasets/NagaYu/cairn-departure-return> |
|
|
| MIT. |
|
|
