---
license: mit
library_name: transformers
pipeline_tag: image-to-text
base_model: Qwen/Qwen2-VL-2B-Instruct
tags:
  - arxiv:2608.00799
  - cad
  - reverse-engineering
  - cadquery
  - vision-language
---

# CADENA

CADENA reconstructs a 3D mesh as a parametric CAD program. Instead of emitting
the whole program in one pass, it grows the operation sequence one step at a
time, executing the partial program and comparing the target with the currently
built geometry before choosing the next operation.

- Paper: [arXiv:2608.00799](https://arxiv.org/abs/2608.00799)
- Code: <https://github.com/zhemdi/cadena>
- Benchmark: <https://huggingface.co/datasets/kulibinai/cadena-bench>

## Checkpoints

This repository holds both training stages as subfolders.

| Subfolder | Stage |
| --- | --- |
| `sft` | Supervised, final checkpoint of the second stage |
| `rl` | Reinforcement learning against executed geometry — the paper's CADENA-RL |

```python
from transformers import AutoModelForVision2Seq, AutoProcessor

model = AutoModelForVision2Seq.from_pretrained("kulibinai/cadena", subfolder="rl")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
```

Or fetch one stage only:

```bash
hf download kulibinai/cadena --include 'rl/*' --local-dir ./ckpt
```

Both are Qwen2-VL-2B policies, 4.4 GB each in bfloat16.

## Input

The model observes eight renders of the target tiled into a single image: six
axis-aligned views and two isometric ones. The target occupies the green
channel and the geometry built so far the red channel, so the residual — what
is still missing — is what the policy actually reads.

## Results

CADENA-RL outperforms prior methods on DeepCAD, Fusion 360, MCB and
CADENA-Bench. The margin grows with geometric complexity: it is modest on
sketch–extrude corpora and substantially larger on real mechanical parts.
Reinforcement learning against executed geometry improves reconstruction
accuracy while lowering the invalid rate, since an operation that fails to
build receives no reward.

See the paper for the full tables.

## Usage

Inference is stepwise and needs the DSL runtime, so use the repository rather
than a bare `generate` call:

```bash
git clone https://github.com/zhemdi/cadena && cd cadena
pip install -r requirements.txt
MODEL_PATH=./ckpt/rl DATASET_PATH=data/stepwise_hf/wrapped/meshes ./inference/run.sh
```

Defaults reproduce the paper's main-results setting: greedy decoding, an
operation budget of 20, `E = 12` candidates per operation, and the returned
program is the prefix with the highest IoU against the target.

## Limitations

- Programs are CadQuery, so CADENA inherits its limitations; in corner cases a
  construction tree valid in CadQuery does not transfer to industrial CAD.
- Accuracy is uneven across part families — revolved and patterned geometry
  (gears, bearings, springs, fasteners) remains the hardest.

## License

MIT.