Xianghe Wang — accepted AI-conference papers

2026

FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation

Zichen Tang, Haihong E, Rongjin Li, Jiacheng Liu, Linwei Jia, Zhuodi Hao +15

AAAI 2026technical

We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to existing benchmarks, our work delivers three major advancements. (1) Scenario Awareness: 57.9% of 1,200 expert-annotated pro

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2026

Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning

Rongjin Li, Zichen Tang, Xianghe Wang, Xinyi Hu, Zhengyu Wang, Zhengyu Lu +6

ICLR 2026poster

With the rapid progress of multimodal large language models (MLLMs), AI already performs well at literature retrieval and certain reasoning tasks, serving as a capable assistant to human researchers, yet it remains far from autonomous research. The fundamental reason is that current work on scholarl…

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