1 citations · 1 across the 10 of their papers we have counts for
11 papers
Can World Simulators Reason? Gen-ViRe: A Generative Visual Reasoning Benchmark
Xinxin Liu, Zhaopan Xu, Ming Li +3
While Chain-of-Thought (CoT) prompting enables sophisticated symbolic reasoning in LLMs, it remains confined to discrete text and cannot simulate the continuous, physics-governed d…
MDK12-Bench: A Comprehensive Evaluation of Multimodal Large Language Models on Multidisciplinary Exams
Pengfei Zhou, Xiaopeng Peng, Fanrui Zhang +18
Multimodal large language models (MLLMs), which integrate language and visual cues for problem-solving, are crucial for advancing artificial general intelligence (AGI). However, cu…
Sekai: A Video Dataset towards World Exploration
Zhen Li, Chuanhao Li, Xiaofeng Mao +17
Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well…
Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
Yansheng Qiu, Li Xiao, Zhaopan Xu +3
The goal of achieving Artificial General Intelligence (AGI) is to imitate humans and surpass them. Models such as OpenAI's o1, o3, and DeepSeek's R1 have demonstrated that large la…
AI Idea Bench 2025: AI Research Idea Generation Benchmark
Yansheng Qiu, Haoquan Zhang, Zhaopan Xu +4
Large-scale Language Models (LLMs) have revolutionized human-AI interaction and achieved significant success in the generation of novel ideas. However, current assessments of idea…
MDK12-Bench: A Multi-Discipline Benchmark for Evaluating Reasoning in Multimodal Large Language Models
Pengfei Zhou, Fanrui Zhang, Xiaopeng Peng +17
Multimodal reasoning, which integrates language and visual cues into problem solving and decision making, is a fundamental aspect of human intelligence and a crucial step toward ar…
MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification
Zhaopan Xu, Pengfei Zhou, Jiaxin Ai +6
Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, where the identification of process errors is vital for improving this ability. Recent…
ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges
Jiaxin Ai, Pengfei Zhou, Zhaopan Xu +8
As multi-modal large language models (MLLMs) frequently exhibit errors when solving scientific problems, evaluating the validity of their reasoning processes is critical for ensuri…
PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models
Zhaopan Xu, Pengfei Zhou, Weidong Tang +7
Multimodal large language models (MLLMs) have achieved remarkable success in vision-language tasks, but their reliance on vast, internet-sourced data raises significant privacy and…
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Pengfei Zhou, Xiaopeng Peng, Jiajun Song +15
Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a ch…
GSTAM: Efficient Graph Distillation with Structural Attention-Matching
Arash Rasti-Meymandi, Ahmad Sajedi, Zhaopan Xu +1
Graph distillation has emerged as a solution for reducing large graph datasets to smaller, more manageable, and informative ones. Existing methods primarily target node classificat…