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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.23178 |
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| _version_ | 1866912611611705344 |
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| author | Qin, Tian Chen, Yuhan Wang, Zhiwei Xu, Zhi-Qin John |
| author_facet | Qin, Tian Chen, Yuhan Wang, Zhiwei Xu, Zhi-Qin John |
| contents | Transformers are able to perform reasoning tasks, however the intrinsic mechanism remains widely open. In this paper we propose a set of information propagation rules based on Transformers and utilize symbolic reasoning tasks to theoretically analyze the limit reasoning steps. We show that the limit number of reasoning steps is between $O(3^{L-1})$ and $O(2^{L-1})$ for a model with $L$ attention layers in a single-pass. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Limit Analysis for Symbolic Multi-step Reasoning Tasks with Information Propagation Rules Based on Transformers Qin, Tian Chen, Yuhan Wang, Zhiwei Xu, Zhi-Qin John Artificial Intelligence Transformers are able to perform reasoning tasks, however the intrinsic mechanism remains widely open. In this paper we propose a set of information propagation rules based on Transformers and utilize symbolic reasoning tasks to theoretically analyze the limit reasoning steps. We show that the limit number of reasoning steps is between $O(3^{L-1})$ and $O(2^{L-1})$ for a model with $L$ attention layers in a single-pass. |
| title | Limit Analysis for Symbolic Multi-step Reasoning Tasks with Information Propagation Rules Based on Transformers |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23178 |