---
license: apache-2.0
language:
- en
tags:
- floydnet
- diffusion-models
- ARC-AGI
---


# FloydARC (ARC-AGI Reasoning)

## Model Summary

**FloydARC** is a neural algorithmic reasoning model adapted from FloydNet for the **ARC-AGI** benchmark.
This checkpoint is trained primarily on ARC-style synthetic and curated data, and is designed to solve ARC tasks via **iterative refinement and test-time adaptation**, rather than large-scale web pretraining.

Among models trained mainly on ARC-like data, FloydARC achieves **state-of-the-art performance** on both ARC-AGI-1 and ARC-AGI-2, significantly narrowing the gap to very large proprietary models.

---

## Performance

FloydARC demonstrates strong generalization on ARC benchmarks under standard evaluation protocols.

**ARC-AGI benchmark results:**

| Model        | #Params | ARC-AGI-1 | ARC-AGI-2 |
| ------------ | ------: | --------: | --------: |
| VARC         |     73M |      60.4 |      11.1 |
| Loop-ViT     |   11.2M |      61.2 |      10.3 |
| HRM          |     27M |      40.3 |       5.0 |
| **FloydARC** |  153.7M |  **70.5** |  **15.3** |



---

## Model Details

* **Model ID**: `ocxlabs/FloydARC`
* **Task**: Abstraction and Reasoning Corpus (ARC-AGI)
* **Architecture**: FloydNet-based global relational reasoning with looped refinement
* **Input / Output**: ARC grid-based visual reasoning (query canvas → predicted answer canvas)
* **License**: Apache 2.0

---

## Usage: Inference & Evaluation

This checkpoint is intended for **research and evaluation use** on ARC-AGI. Full reproduction of reported results requires multi-GPU inference with test-time training.

### 1. Download checkpoint

Download the pretrained checkpoint from Hugging Face:

```
https://huggingface.co/ocxlabs/FloydARC
```

Place the downloaded folder anywhere on disk and pass its path via `--ckpt_path`.

---

### 2. Prepare ARC evaluation data

Place the original ARC JSON files under `rawdata/`, then preprocess:

```bash
python -m scripts.process_data \
  --input_dir ./rawdata/ARC-AGI-1_evaluation/ \
  --output_dir ./preprocessed/arc1 \
  --split test
```

Repeat with `ARC-AGI-2_evaluation` for ARC-AGI-2.

---

### 3. Run inference with Test-Time Training (recommended)

```bash
python -m scripts.TTT \
  --ckpt_path /path/to/floydarc_ckpt \
  --subset arc1 \
  --output_dir ./output/TTT_results
```

Notes:

* Default configuration uses **8 GPUs on a single node**
* LoRA-based TTT is enabled by default and recommended
* For ARC-AGI-2, set `--subset arc2`

---

### 4. Ensembling & visualization

For reproducible evaluation and qualitative inspection:

```bash
python -m scripts.analyze \
  --result-folder ./output/TTT_results \
  --subset arc1 \
  --out-html output/arc1_results.html
```

Multiple result folders can be passed to enable max-voting ensembling.