---
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.