Financial numerical reasoning demands rigorous adherence to domain-specific logic and precise evidence foundation. However, large language models (LLMs) are prone to forced generation when confronting ambiguous evidence or complex recursive dependencies, often hallucinating values to bridge information gaps. To address this, we propose graph-bounded financial reasoning (GBFR), a neuro-symbolic framework that imposes semantic and structural constraints via a financial metric knowledge graph (FMKG). Unlike sequential generation paradigms, our approach employs a parallel graph-constrained reasoning algorithm that orchestrates specialized operators to simultaneously explore heterogeneous derivation paths of complex financial metrics. Through cross-path verification, the framework aggregates only semantically consistent results, ensuring reasoning is bounded by available context. Crucially, this approach enables safe abstention by distinguishing genuine data absence from retrieval failure, thereby preventing ungrounded fabrication. To evaluate this capability, we further construct counterfactual samples by perturbing entities, times, and metrics to synthesize unanswerable scenarios. Empirical evaluations on standard benchmarks demonstrate that GBFR significantly outperforms state-of-the-art baselines.

“术语分布呈现长尾特性。为了有效提取低频术语,本文提出了一种基于词向量的自适应术语抽取方法。该方法使用基于假设检验的统计方法,自适应地确定筛选阈值,通过逐步合并文本的强关联性字符串获得候选术语,避免了因固定阈值导致的低频术语遗漏问题;其后,本文基于掩码语言模型获得未登录候选术语的词向量,并通过融合词典知识的密度聚类算法获得候选术语归属的领域簇,将归属于目标领域簇的候选术语认定为领域术语。实验结果表明,我们的方法不仅在但值上优于对比方法,而且在不同体裁的文本中表现更为稳定。该方法能够全面有效地抽取出低频术语,实现领域术语的高质量提取。”