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), mathematically bounded by a novel Double-Lock Grounding algorithm. Recognizing that upstream parsing anomalies inevitably occur, we introduce the VeNRA Sentinel: a 3-billion parameter SLM trained to forensically audit Python execution traces with only one token test budget. To train this model, we avoid traditional generative hallucination datasets in favor of Adversarial Simulation, programmatically sabotaging golden financial records to simulate production-level ``Ecological Errors'' (e.g., Logic Code Lies and Numeric Neighbor Traps). Finally, to optimize the Sentinel under strict latency budgets, we utilize a single-pass classification paradigm with optional post thinking for debug. We identify the phenomenon of Loss Dilution in Reverse-Chain-of-Thought training and present a novel, OOM-safe Micro-Chunking loss algorithm to stabilize gradients under extreme differential penalization.

翻译：标准检索增强生成（RAG）架构在高风险金融领域存在两大根本性局限：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问题（例如因上下文邻近性将“净利润”映射至“净销售额”）。在确定性领域中，99%的准确率意味着0%的操作可信度。为实现零幻觉的金融推理，我们提出可验证数值推理智能体（VeNRA）。VeNRA通过严格类型化的通用事实账本（UFL）将RAG范式从检索概率性文本转变为检索确定性变量，并采用新型双锁锚定算法进行数学约束。考虑到上游解析异常不可避免，我们引入VeNRA哨兵系统：一个30亿参数的专用语言模型（SLM），其训练目标是以单次令牌测试预算对Python执行轨迹进行法证级审计。为训练该模型，我们摒弃传统生成式幻觉数据集，采用对抗性仿真方法，通过程序化篡改标准财务记录来模拟生产环境中的“生态错误”（例如逻辑代码欺骗与数值邻域陷阱）。最后，为在严格延迟预算下优化哨兵系统，我们采用单次分类范式并辅以可选的后续思考机制进行调试。我们揭示了反向思维链训练中的损失稀释现象，并提出一种新型、内存安全（OOM-safe）的微块损失算法，以在极端差异化惩罚条件下稳定梯度。