CLER: Improving Multimodal Financial Reasoning by Cross-MLLM Error Reflection

Recent advances in Multimodal Large Language Models (MLLMs) have enabled joint reasoning over financial textual and visual inputs. However, they still struggle with financial terminology, logical consistency, and numeric computations. Moreover, while closed-source models perform well on reasoning tasks, their high inference costs limit the scalable usage in real-world financial applications. We thus propose a cost-effective framework, CLER, combining contrastive retrieval with step-wise reflection, to improve reasoning performance. Also, the reasoning cost only generated in test stage when using commercial large models. CLER leverages FinErrorSet, a dataset of 8,000+ mistake-correction pairs from diverse open-source MLLMs. A fine-grained retriever is trained to identify structurally relevant errors for self-correction through individual reflection. Experiments on three multimodal benchmarks show that CLER consistently outperforms other baselines. To our knowledge, CLER is the first framework to use cross-model errors for financial reasoning.