Standard Retrieval-Augmented Generation (RAG) architectures fail in high-stakes financial domains due to two fundamental limitations: the inherent arithmetic incompetence of Large Language Models (LLMs) and the distributional semantic conflation of dense vector retrieval (e.g., mapping "Net Income" to "Net Sales" due to contextual proximity). In deterministic domains, a 99% accuracy rate yields 0% operational trust. To achieve zero-hallucination financial reasoning, we introduce the Verifiable Numerical Reasoning Agent (VeNRA). VeNRA shifts the RAG paradigm from retrieving probabilistic text to retrieving deterministic variables via a strictly typed Universal Fact Ledger (UFL). We mathematically bound this ledger using a novel Double-Lock Grounding algorithm. Coupled with deterministic Python execution, this neuro-symbolic routing compresses systemic hallucination rates to a near-zero 1.2%. Recognising that upstream parsing anomalies inevitably occur, we introduce the VeNRA Sentinel: a 3-billion parameter SLM trained to forensically audit candidate using a single-token inference budget with optional post-hoc reasoning. To train the Sentinel, we steer away from traditional hallucination datasets in favour of Adversarial Simulation, programmatically sabotaging financial records to simulate Ecological Errors. The compact Sentinel consequently outperforms 70B+ frontier models in error detection. Through Loss Dilution phenomenon in Reverse-CoT training, we present a novel Micro-Chunking loss algorithm to stabilise gradients under extreme verdict penalisation, yielding a 28x latency speedup without sacrificing forensic rigor.

翻译：标准检索增强生成（RAG）架构在高风险金融领域存在两个根本性缺陷：大型语言模型（LLMs）固有的算术能力不足，以及稠密向量检索的分布语义混淆（例如，由于上下文邻近性将“净利润”映射到“净销售额”）。在确定性领域，99%的准确率意味着0%的操作信任度。为实现零幻觉的金融推理，我们引入了可验证数值推理代理（VeNRA）。VeNRA将RAG范式从检索概率性文本转变为通过严格类型化的通用事实账本（UFL）检索确定性变量。我们采用一种新颖的双锁锚定算法对该账本进行数学约束。结合确定性的Python执行，这种神经符号路由将系统性幻觉率压缩至接近零的1.2%。认识到上游解析异常不可避免，我们引入了VeNRA哨兵：一个30亿参数的SLM，经过训练以单令牌推理预算（可选事后推理）对候选结果进行法证审计。为训练哨兵模型，我们摒弃传统的幻觉数据集，转而采用对抗性模拟方法，通过程序化篡改财务记录来模拟生态错误。因此，这个紧凑的哨兵模型在错误检测方面超越了700亿参数以上的前沿模型。通过逆向思维链训练中的损失稀释现象，我们提出了一种新颖的微分块损失算法，以在极端判决惩罚下稳定梯度，在不牺牲法证严谨性的前提下实现了28倍的延迟加速。