Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.01072 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917454126514176 |
|---|---|
| author | Cao, Haotian Merz, Garrett Cranmer, Kyle Shiu, Gary |
| author_facet | Cao, Haotian Merz, Garrett Cranmer, Kyle Shiu, Gary |
| contents | We study the use of transformers to reconstruct the compositions of tensor products of two-dimensional rational conformal field theories (RCFTs) based on their low-energy spectra. The task is challenging due to its combinatorial nature. The constituent theories are characterized by their central charges and affine Lie algebra labels. We achieve 98% accuracy in recovering the constituents of tensor products theories constructed from Wess-Zumino-Witten models. We further demonstrate that our method generalizes to CFTs with larger central charge and unseen classes of RCFTs by adding a small number of out-of-domain examples. Our results show that transformers are effective at this task and point towards a new tool for bulk reconstruction in AdS/CFT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reconstructing conformal field theoretical compositions with Transformers Cao, Haotian Merz, Garrett Cranmer, Kyle Shiu, Gary High Energy Physics - Theory Machine Learning We study the use of transformers to reconstruct the compositions of tensor products of two-dimensional rational conformal field theories (RCFTs) based on their low-energy spectra. The task is challenging due to its combinatorial nature. The constituent theories are characterized by their central charges and affine Lie algebra labels. We achieve 98% accuracy in recovering the constituents of tensor products theories constructed from Wess-Zumino-Witten models. We further demonstrate that our method generalizes to CFTs with larger central charge and unseen classes of RCFTs by adding a small number of out-of-domain examples. Our results show that transformers are effective at this task and point towards a new tool for bulk reconstruction in AdS/CFT. |
| title | Reconstructing conformal field theoretical compositions with Transformers |
| topic | High Energy Physics - Theory Machine Learning |
| url | https://arxiv.org/abs/2605.01072 |