神经符号架构让生成AI更会推理、更透明，效果显著提升。
Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations
- 用神经网络+符号系统融合方式增强模型推理能力
- 神经主导的混合架构在各项指标上表现最优
- 适合关注可解释性与泛化能力的研究者
神经符号人工智能（NSAI）通过结合深度学习处理大规模非结构化数据的能力与符号方法的结构化推理优势，实现了对通用性、推理能力和可扩展性的提升，并缓解了透明度与数据效率等关键问题。本文系统研究了多种NSAI架构，分析其融合神经与符号组件的独特路径。进一步考察了检索增强生成、图神经网络、强化学习及多智能体系统等前沿技术与NSAI范式的契合度。基于通用性、推理能力、迁移性和可解释性等维度评估各架构，结果表明‘神经主导—符号辅助—神经反馈’模型在所有指标上均优于其他架构，与当前顶尖研究一致，验证了该类架构在整合多智能体系统等先进技术中的有效性。
原文摘要 · Abstract (English)
Neuro-symbolic artificial intelligence (NSAI) represents a transformative approach in artificial intelligence (AI) by combining deep learning's ability to handle large-scale and unstructured data with the structured reasoning of symbolic methods. By leveraging their complementary strengths, NSAI enhances generalization, reasoning, and scalability while addressing key challenges such as transparency and data efficiency. This paper systematically studies diverse NSAI architectures, highlighting their unique approaches to integrating neural and symbolic components. It examines the alignment of contemporary AI techniques such as retrieval-augmented generation, graph neural networks, reinforcement learning, and multi-agent systems with NSAI paradigms. This study then evaluates these architectures against comprehensive set of criteria, including generalization, reasoning capabilities, transferability, and interpretability, therefore providing a comparative analysis of their respective strengths and limitations. Notably, the Neuro > Symbolic < Neuro model consistently outperforms its counterparts across all evaluation metrics. This result aligns with state-of-the-art research that highlight the efficacy of such architectures in harnessing advanced technologies like multi-agent systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。