{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:46Z","timestamp":1758672886958,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding and commonsense reasoning, yet they often struggle with constraint satisfaction in planning problems. Previous studies relying on test-time improvement with self-evaluation fail to address this limitation effectively. In this work, we identify this critical gap and propose a novel neuro-symbolic framework, Reinforced Neuro-Symbolic Planning (\\algo), that enhances LLM-powered planning by incorporating a symbolic verifier. The verifier provides explicit feedback on constraint satisfaction, enabling iterative refinement of the state evaluation. Specifically, we utilize the outcome feedback from each logical goal to update the process value along planning paths through a reinforcement value function maximization objective. We further employ T-norms to aggregate the satisfaction levels of multiple constraints, which provided more effective guidance for the test-time search. Our framework bridges the strengths of neural and symbolic methods, leveraging the generative power of LLMs while ensuring rigorous adherence to constraints through symbolic verification. Extensive experiments demonstrate that our approach significantly improves planning accuracy and constraint satisfaction across various domains, outperforming traditional self-evaluation methods. It highlights the potential of hybrid neuro-symbolic systems to address complex constrained planning tasks.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/682","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"6129-6137","source":"Crossref","is-referenced-by-count":0,"title":["Breaking the Self-Evaluation Barrier: Reinforced Neuro-Symbolic Planning with Large Language Models"],"prefix":"10.24963","author":[{"given":"Jie-Jing","family":"Shao","sequence":"first","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Jie","family":"You","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"},{"name":"School of Artificial Intelligence, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohao","family":"Cai","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s ArkLab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quanyu","family":"Dai","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s ArkLab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Dong","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s ArkLab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan-Zhe","family":"Guo","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"},{"name":"School of Intelligence Science and Technology, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:50Z","timestamp":1758627290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/682"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/682","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}