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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.01154 |
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| _version_ | 1866918477895303168 |
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| author | Talley, Caleb Tibrewal, Vedant Adekunle, Seun Dong, Weiwen Wu, Xinyu Sheikh, Fariha |
| author_facet | Talley, Caleb Tibrewal, Vedant Adekunle, Seun Dong, Weiwen Wu, Xinyu Sheikh, Fariha |
| contents | ARC-AGI-2 is a benchmark of human-intuitive visual puzzles that measures a machine's ability to generalize from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts. In this paper, we discuss our approach to solving the ARC-AGI-2 puzzles with TinyLM, with additional fine-tuning at test time, including Test-Time-Training (TTT) and Products of Experts (POE). Our model achieves 96.1% accuracy on the training set and 21.7% accuracy on the evaluation set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Perspective Transformers in ARC-AGI-2 Challenge Talley, Caleb Tibrewal, Vedant Adekunle, Seun Dong, Weiwen Wu, Xinyu Sheikh, Fariha Machine Learning Artificial Intelligence ARC-AGI-2 is a benchmark of human-intuitive visual puzzles that measures a machine's ability to generalize from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts. In this paper, we discuss our approach to solving the ARC-AGI-2 puzzles with TinyLM, with additional fine-tuning at test time, including Test-Time-Training (TTT) and Products of Experts (POE). Our model achieves 96.1% accuracy on the training set and 21.7% accuracy on the evaluation set. |
| title | Multi-Perspective Transformers in ARC-AGI-2 Challenge |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.01154 |