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
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:
