Financial question answering requires both a concise answer grounded in news and a supporting excerpt from heterogeneous financial statements. We present a training-free modular pipeline that separates the two objectives, where the evidence module relies on the frozen output of the answer module. The Answer module constructs a high-precision core using Pairwise Selection, synthesizes a higher-recall extended answer from five candidate views, and selectively transfers supported content through Delta Augmentation and Claim Pruning. The Evidence module applies generate–refine–MBR selection to obtain a primary evidence candidate, independently generates an alternative with Qwen3.6-Think, and replaces the default only under a conservative decision policy. We evaluate answer module performance on 76 examples scored by a rubric-guided LLM judge. On the 0–4 judging scale, Pairwise Selection raises the average answer score from 2.316 to 2.540 (+0.224), while Delta Augmentation and Claim Pruning provide further gains of +0.079 and +0.066, respectively.

Selective compliance is important for financial LLM applications: an applicable context rule should override the LLM’s parametric rule, while an inapplicable rule should be ignored. Though previous studies recognize the importance of rule selection, dedicated evaluations tailored specifically to the financial domain remain absent. To bridge this gap, we build a controlled benchmark based on the CFLUE dataset, where intervention rules are synthesized and FOL logic is applied to ensure the correctness of the new derived answer. Furthermore, we designed three evaluation settings covering clean inputs, noisy inputs, and multi-hop reasoning scenarios. We conduct a systematic comparison across different rule selection strategies and models. Experiments show that: (1) For recent advanced LLMs, single-pass integrated reasoning approach (CAR-Joint) consistently outperforms the decoupled ‘route-then-answer’ approach (CAR-Route). (2) Our proposed post-hoc review method, CAR-Joint-Review, consistently outperforms CAR-Joint. Similarly, pre-hoc routing via the parametric rule elicitation method CAR-Elicit-Route surpasses CAR-Route in multi-hop settings. The performance gains are model-dependent. These findings reveal the trade-offs across various methods and LLMs in financial selective compliance.