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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.14693 |
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| _version_ | 1866908732496019456 |
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| author | Gao, Zitian Chen, Lynx Xiao, Yihao Xing, He Tao, Ran Luo, Haoming Zhou, Joey Dai, Bryan |
| author_facet | Gao, Zitian Chen, Lynx Xiao, Yihao Xing, He Tao, Ran Luo, Haoming Zhou, Joey Dai, Bryan |
| contents | Universal transformers (UTs) have been widely used for complex reasoning tasks such as ARC-AGI and Sudoku, yet the specific sources of their performance gains remain underexplored. In this work, we systematically analyze UTs variants and show that improvements on ARC-AGI primarily arise from the recurrent inductive bias and strong nonlinear components of Transformer, rather than from elaborate architectural designs. Motivated by this finding, we propose the Universal Reasoning Model (URM), which enhances the UT with short convolution and truncated backpropagation. Our approach substantially improves reasoning performance, achieving state-of-the-art 53.8% pass@1 on ARC-AGI 1 and 16.0% pass@1 on ARC-AGI 2. Our code is avaliable at https://github.com/UbiquantAI/URM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Universal Reasoning Model Gao, Zitian Chen, Lynx Xiao, Yihao Xing, He Tao, Ran Luo, Haoming Zhou, Joey Dai, Bryan Artificial Intelligence Universal transformers (UTs) have been widely used for complex reasoning tasks such as ARC-AGI and Sudoku, yet the specific sources of their performance gains remain underexplored. In this work, we systematically analyze UTs variants and show that improvements on ARC-AGI primarily arise from the recurrent inductive bias and strong nonlinear components of Transformer, rather than from elaborate architectural designs. Motivated by this finding, we propose the Universal Reasoning Model (URM), which enhances the UT with short convolution and truncated backpropagation. Our approach substantially improves reasoning performance, achieving state-of-the-art 53.8% pass@1 on ARC-AGI 1 and 16.0% pass@1 on ARC-AGI 2. Our code is avaliable at https://github.com/UbiquantAI/URM. |
| title | Universal Reasoning Model |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.14693 |