NSA: Neuro-symbolic ARC Challenge
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917548463751168 |
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| author | Batorski, Paweł Brinkmann, Jannik Swoboda, Paul |
| author_facet | Batorski, Paweł Brinkmann, Jannik Swoboda, Paul |
| contents | The Abstraction and Reasoning Corpus (ARC) evaluates general reasoning capabilities that are difficult for both machine learning models and combinatorial search methods. We propose a neuro-symbolic approach that combines a transformer for proposal generation with combinatorial search using a domain-specific language. The transformer narrows the search space by proposing promising search directions, which allows the combinatorial search to find the actual solution in short time. We pre-train the trainsformer with synthetically generated data. During test-time we generate additional task-specific training tasks and fine-tune our model. Our results surpass comparable state of the art on the ARC evaluation set by 27% and compare favourably on the ARC train set. We make our code and dataset publicly available at https://github.com/Batorskq/NSA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04424 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NSA: Neuro-symbolic ARC Challenge Batorski, Paweł Brinkmann, Jannik Swoboda, Paul Artificial Intelligence Computation and Language The Abstraction and Reasoning Corpus (ARC) evaluates general reasoning capabilities that are difficult for both machine learning models and combinatorial search methods. We propose a neuro-symbolic approach that combines a transformer for proposal generation with combinatorial search using a domain-specific language. The transformer narrows the search space by proposing promising search directions, which allows the combinatorial search to find the actual solution in short time. We pre-train the trainsformer with synthetically generated data. During test-time we generate additional task-specific training tasks and fine-tune our model. Our results surpass comparable state of the art on the ARC evaluation set by 27% and compare favourably on the ARC train set. We make our code and dataset publicly available at https://github.com/Batorskq/NSA. |
| title | NSA: Neuro-symbolic ARC Challenge |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2501.04424 |