Multimodal large language models (MLLMs) have shown increasing potential for applications in the financial domain. However, the challenges posed by real-world financial scenarios, including high-information-density, strong noise interference, and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing financial multimodal benchmarks. To this end, we propose UniFinEval, the first unified multimodal financial evaluation benchmark designed for high-density financial information environments, covering three modalities: text, image, and video. UniFinEval abstracts three progressive core scenarios from perception to reasoning and decision-making, and further refines them into six task categories. It contains 5,567 high-quality Chinese and English question–answer (QA) pairs constructed by financial experts. We systematically evaluate 10 mainstream MLLMs under zero-shot and chain-of-thought (CoT) settings. Experimental results show that Gemini-3-Pro achieves the best overall performance, but still exhibits a significant gap compared with financial experts. Further error analysis reveals the capability deficiencies of MLLMs in financial scenarios. UniFinEval aims to systematically evaluate the capabilities of MLLMs in real financial scenarios, thereby promoting the application of MLLMs in the financial domain. The related code and data are available at https://github.com/aifinlab/UniFinEval.

Multimodal large language models show significant potential in mathematical reasoning, yet they remain constrained by a scarcity of high-quality training data for visually grounded problems. Existing approaches commonly suffer from narrow coverage, image-text inconsistency, and heavy imitation of seed problems. To address these limitations, this paper proposes AgSynth, a multi-agent collaborative reverse synthesis framework. It first constructs solution logic programmatically and renders the corresponding images, then generates problem statements backward from the visual facts, thereby embedding answer correctness and image-text consistency directly into the generation pipeline. Meanwhile, AgSynth distills the core competencies tested in seed problems and performs divergent reconstruction, enabling synthesized problems to be substantially novel in both structure and visual presentation. Based on this framework, we construct the AgSynth-10K dataset, comprising 10,477 multimodal mathematical reasoning problems that have undergone two-tier quality control, spanning four major domains: geometry, algebra, combinatorics, and number theory. Experiments demonstrate that reinforcement learning training on AgSynth-10K significantly improves model performance on reasoning-intensive benchmarks (WeMath +7.42, LogicVista+9.15), validating the effectiveness of this traceable synthesis paradigm. The framework is publicly available at https://github.com/Yaxuan7690/AgSynth.

Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question–answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical scenario depths: Financial Knowledge & Data Analysis, Financial Analysis & Decision Support, and Financial Risk Control & Asset Optimization. We evaluate 21 state-of-the-art MLLMs in a zero-shot setting. The top model, Qwen-VL-max, achieves an overall accuracy of 76.3%, outperforming non-expert humans but trailing financial experts by over 14 percentage points. Our error analysis uncovers six recurring failure modes—including cross-modal misalignment, hallucinations, and lapses in business-process reasoning—that highlight critical avenues for future research. VisFinEval aims to accelerate the development of robust, domain-tailored MLLMs capable of seamlessly integrating textual and visual financial information. The data and the code are available at https://github.com/SUFE-AIFLM-Lab/VisFinEval.