Academic Journal
KG -augmented executable CoT for mathematical coding.
| Τίτλος: | KG -augmented executable CoT for mathematical coding. |
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| Συγγραφείς: | Chen X; School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China., An J; School of Statistics, Chengdu University of Information Technology, Chengdu, 610103, China. Electronic address: anjunxiu@cuit.edu.cn., Guo J; College of Applied Mathematics, Chengdu University of Information Technology, Chengdu, 610225, China., Wang L; School of Artificial Intelligence, Beihang University, Beijing, 100191, China., Guo J; Department of Computing, The Hong Kong Polytechnic University, Hong Kong, 999077, China. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Oct; Vol. 202, pp. 109006. Date of Electronic Publication: 2026 Apr 19. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Natural Language Processing* , Mathematics* , Neural Networks, Computer*, Large Language Models ; Humans |
| Περίληψη: | In recent years, large language models (LLMs) have excelled in natural language processing tasks, but face significant challenges in complex reasoning tasks such as mathematical reasoning and code generation. To address these limitations, we introduce the Neuro-Symbolic paradigm into the field of mathematical reasoning and propose KGA-ECoT, a Knowledge Graph Augmented Executable Chain-of-Thought, which decomposes problems into a structured task graph, incorporates an adaptive on-demand GraphRAG mechanism for precise knowledge retrieval from Symbolic reasoning libraries, and generates verifiable code to ensure computational accuracy. This not only improves performance but also enhances the model's interpretability and verifiability, reducing its "black-box" nature. Evaluations on multiple mathematical reasoning benchmarks demonstrate that KGA-ECoT significantly outperforms existing prompting methods, achieving absolute accuracy improvements ranging from several to over ten percentage points. Further analysis confirms the critical roles of GraphRAG and external code execution, validating the efficacy of the Neuro-Symbolic paradigm for complex reasoning. Comprehensive implementation details and full prompt templates are provided in the appendix to ensure reproducibility. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Chain-of-thought; Knowledge graphs; LLMs; Mathematical reasoning; Prompt engineering |
| Entry Date(s): | Date Created: 20260427 Date Completed: 20260718 Latest Revision: 20260718 |
| Update Code: | 20260718 |
| DOI: | 10.1016/j.neunet.2026.109006 |
| PMID: | 42044587 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.109006 |