Abstract
This study investigates an explainable reasoning method for financial decision-making based on knowledge-enhanced large language model agents. To address the limitations of traditional financial decision methods that rely on parameterized knowledge, lack factual consistency, and miss reasoning chains, an integrated framework is proposed that combines external knowledge retrieval, semantic representation, and reasoning generation. The method first encodes financial texts and structured data to obtain semantic representations, and then retrieves task-related information from external knowledge bases using similarity computation. Internal representations and external knowledge are combined through weighted fusion, which ensures fluency while improving factual accuracy and completeness of generated content. In the reasoning stage, a multi-head attention mechanism is introduced to construct logical chains, allowing the model to present transparent causal relationships and traceability during generation. Finally, the model jointly optimizes task objectives and explanation consistency objectives, which enhances predictive performance and reasoning interpretability. Experiments on financial text processing and decision tasks show that the method outperforms baseline approaches in accuracy, text generation quality, and factual support, verifying the effectiveness of knowledge enhancement and explainable reasoning. Overall, the proposed approach overcomes the limitations of traditional models in semantic coverage and reasoning transparency, and demonstrates strong practical value in complex financial scenarios.
Abstract (translated)
这项研究探讨了一种基于知识增强的大语言模型代理的可解释推理方法在金融决策中的应用。为了克服传统金融决策方法依赖参数化知识、缺乏事实一致性以及忽略推理链等问题，提出了一种集成了外部知识检索、语义表示和推理生成于一体的框架。该方法首先将财务文本和结构化数据编码为语义表示，并通过相似度计算从外部知识库中检索任务相关的信息。内部表示与外部知识结合时采用加权融合的方式，在保证流畅性的同时，提升了生成内容的事实准确性和完整性。 在推理阶段，引入了多头注意力机制来构建逻辑链，使模型能够在生成过程中呈现透明的因果关系和可追溯性。最后，该模型通过同时优化任务目标和解释一致性目标，不仅提高了预测性能，还增强了推理的可解释性。通过对金融文本处理及决策任务的实验表明，该方法在准确性、文本生成质量和事实支持方面均优于基线方法，验证了知识增强与可解释推理的有效性。 总体而言，提出的这种方法克服了传统模型在语义覆盖和推理透明度方面的局限性，并在复杂的金融场景中展现了强大的实际应用价值。
URL
https://arxiv.org/abs/2512.09440
