Xin-Sheng Chen — accepted AI-conference papers

2026

Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs

Yi Zhang, Bolin Ni, Xin-Sheng Chen, Hengrui Zhang, Yongming Rao, Houwen Peng +4

ICLR 2026poster

Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to a significant gap in data quality for supervised fine-tuning (SFT). Existing open-source datasets are often plagued by widespread noise and a critical deficit in complex reasoning dat…

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2025

RBench-V: A Primary Assessment for Visual Reasoning Models with Multimodal Outputs

Meng-Hao Guo, Xuanyu Chu, Qianrui Yang, Zhe-Han Mo, Yiqing Shen, Pei-lin Li +9

NeurIPS 2025poster

The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini and o3 with their capability to process and generate content across modalities such as text and images, marks a significant milestone in the evolution of intelligence. Systematic evaluation of their mul…

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