collaborators

36 papers

cs.CV2026

VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

Haodong Duan, Xinyu Fang, Junming Yang +41

We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friendly and comprehensive framework f…

cs.CV2026

Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games

Shengyuan Ding, Xilin Wei, Xinyu Fang +4

Deploying multimodal foundation models as closed-loop policies increasingly requires conditioning actions on observations that are no longer visible. However, existing benchmarks e…

cs.CL2026

OpenCompass: A Universal Evaluation Platform for Large Language Models

Maosong Cao, Kai Chen, Haodong Duan +27

In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the…

cs.CV2026

MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence

Sihan Yang, Runsen Xu, Yiman Xie +10

Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the complex physical world. Existing benchmarks, however, probe only single-image relati…

cs.CV2026

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context

Zhaowei Wang, Lishu Luo, Haodong Duan +9

Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-document understanding, video…

cs.CL2026

WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation

Shuangrui Ding, Xuanlang Dai, Long Xing +14

Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still…