Research Paper#Multimodal Large Language Models, Financial Reasoning, Benchmarking🔬 ResearchAnalyzed: Jan 3, 2026 06:22
FinMMDocR: A New Benchmark for Financial Multimodal Reasoning
Analysis
This paper introduces FinMMDocR, a new benchmark designed to evaluate multimodal large language models (MLLMs) on complex financial reasoning tasks. The benchmark's key contributions are its focus on scenario awareness, document understanding (with extensive document breadth and depth), and multi-step computation, making it more challenging and realistic than existing benchmarks. The low accuracy of the best-performing MLLM (58.0%) highlights the difficulty of the task and the potential for future research.
Key Takeaways
- •FinMMDocR is a new benchmark for evaluating MLLMs on financial reasoning.
- •It emphasizes scenario awareness, document understanding, and multi-step computation.
- •The benchmark is designed to be more challenging and realistic than existing ones.
- •Current MLLMs struggle with the benchmark, indicating room for improvement.
Reference
“The best-performing MLLM achieves only 58.0% accuracy.”