# SimVerse / cube1

Cube reconstruction (six-face map): given a blank cross net of a cube and a top-down image of the bottom-face imprints stamped along a roll path, reconstruct the patternId and rotation of every outer face.

- **Records:** 502 levels
- **Modality:** blank cross net image + top-down path-imprint image
- **Output:** `{"faces": {"TOP": {patternId, rotation}, "BOTTOM": ..., ..., "RIGHT": ...}}`

## Loading

```python
from datasets import load_dataset

ds = load_dataset("SimVer-ano/simverse2026", "cube1")
example = ds["test"][0]

system_text = example["prompt"]["system"]
user_text   = example["prompt"]["user"]

# Image paths (relative to this config's root)
blank_net    = example["image_paths"]["blank_net_image"]       # e.g. "images/blank_nets/open.png"
path_seq     = example["image_paths"]["path_sequence_image"]   # e.g. "images/path_sequences/C001_path_sequence.png"

gold_faces = example["answer"]["faces"]                        # {TOP: {...}, BOTTOM: {...}, ...}
```

The image paths in `image_paths` are already relative to this config's root, so you can resolve them as-is.

## Schema

| Field | Type | Description |
|---|---|---|
| `sample_id` | string | Level id, e.g. `"C001"` |
| `__sample_id__` | string | Same as `sample_id` |
| `prompt.system` / `prompt.user` | string | Exact prompt text |
| `net_layout` | string | Net layout name (e.g. `"standard_cross"`) |
| `roll_sequence` | list[string] | Sequence of rolls, each `"N"`/`"S"`/`"E"`/`"W"` |
| `observed_path_faces` | list of face observations | Per-step bottom-face imprints visible in the path image |
| `image_paths.blank_net_image` | string | Blank cross net image path |
| `image_paths.path_sequence_image` | string | Top-down path imprint image path |
| `metadata` | dict | `difficulty`, `move_count`, `tier`, `tier_label`, etc. |
| `net_faces` | list of face dicts | Net cells with their initial patterns and rotations |
| `bottom_faces` | list of stamped face dicts | The imprints with their `(x, y)` positions |
| `slot_sequence` / `required_slots` / `required_count` | various | Which faces the engine ranks for partial-credit scoring |
| `true_solution_faces` | dict | Ground-truth full face map (used for scoring) |
| `answer.faces` | dict | Reference `(patternId, rotation)` per face |
| `legacy_answer` | dict | Pre-v1 bare face map (kept for back-compat) |

## Companion file

`catalog.json` mirrors the original `levels/index.json` — a fat JSON containing every level's full data inline. The frontend demo at <https://github.com/SimVer-ano/simverse2026> reads this file. It is regenerable from `data/*.json` via `python cube1/regenerate_catalog.py` in the repo.

## "?" sentinel rule

Each face's `patternId` is either a string from the per-level allowed-patternId list, or the literal `"?"` meaning "cannot be uniquely determined". When `patternId == "?"`, `rotation` is forced to `0`. The validator scores `?` against `?` as correct; `?` against a concrete pattern (or vice versa) as wrong.

## License

MIT — see [LICENSE](../LICENSE) at the repo root.