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20232026
most citedMMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

6 citations · 16 across the 17 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents

Fanqing Meng, Lingxiao Du, Zijian Wu +46

Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…

cs.CV2026

Gym-V: A Unified Vision Environment System for Agentic Vision Research

Fanqing Meng, Lingxiao Du, Jiawei Gu +9

As agentic systems increasingly rely on reinforcement learning from verifiable rewards, standardized ``gym'' infrastructure has become essential for rapid iteration, reproducibilit…

cs.CV2025

MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Fanqing Meng, Lingxiao Du, Zongkai Liu +12

DeepSeek R1, and o1 have demonstrated powerful reasoning capabilities in the text domain through stable large-scale reinforcement learning. To enable broader applications, some wor…

cs.CV2024★ 1 cited

Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation

Fanqing Meng, Jiaqi Liao, Xinyu Tan +7

Text-to-video (T2V) models like Sora have made significant strides in visualizing complex prompts, which is increasingly viewed as a promising path towards constructing the univers…

cs.CV2024★ 1 cited

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models

Fanqing Meng, Jin Wang, Chuanhao Li +9

The capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LV…

cs.CV2024★ 1 cited

PhyBench: A Physical Commonsense Benchmark for Evaluating Text-to-Image Models

Fanqing Meng, Wenqi Shao, Lixin Luo +8

Text-to-image (T2I) models have made substantial progress in generating images from textual prompts. However, they frequently fail to produce images consistent with physical common…

cs.CV2024

GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices

Quanfeng Lu, Wenqi Shao, Zitao Liu +7

Autonomous Graphical User Interface (GUI) navigation agents can enhance user experience in communication, entertainment, and productivity by streamlining workflows and reducing man…

cs.CV2024★ 6 cited

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Kaining Ying, Fanqing Meng, Jin Wang +19

Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimod…

cs.CV2024★ 4 cited

ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning

Fanqing Meng, Wenqi Shao, Quanfeng Lu +4

Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, line…

cs.CV2023★ 2 cited

TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models

Wenqi Shao, Meng Lei, Yutao Hu +8

Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Goog…