ProRAC: A Neuro-symbolic Method for Reasoning about Actions with LLM-based Progression
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908664974016512 |
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| author | Wu, Haoyong Liu, Yongmei |
| author_facet | Wu, Haoyong Liu, Yongmei |
| contents | In this paper, we propose ProRAC (Progression-based Reasoning about Actions and Change), a neuro-symbolic framework that leverages LLMs to tackle RAC problems. ProRAC extracts fundamental RAC elements including actions and questions from the problem, progressively executes each action to derive the final state, and then evaluates the query against the progressed state to arrive at an answer. We evaluate ProRAC on several RAC benchmarks, and the results demonstrate that our approach achieves strong performance across different benchmarks, domains, LLM backbones, and types of RAC tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProRAC: A Neuro-symbolic Method for Reasoning about Actions with LLM-based Progression Wu, Haoyong Liu, Yongmei Artificial Intelligence Computation and Language In this paper, we propose ProRAC (Progression-based Reasoning about Actions and Change), a neuro-symbolic framework that leverages LLMs to tackle RAC problems. ProRAC extracts fundamental RAC elements including actions and questions from the problem, progressively executes each action to derive the final state, and then evaluates the query against the progressed state to arrive at an answer. We evaluate ProRAC on several RAC benchmarks, and the results demonstrate that our approach achieves strong performance across different benchmarks, domains, LLM backbones, and types of RAC tasks. |
| title | ProRAC: A Neuro-symbolic Method for Reasoning about Actions with LLM-based Progression |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.15069 |