192 citations · 424 across the 155 of their papers we have counts for
163 papers
TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models
Xuanyi Liu, Haofeng Wang, Ruiqi Li +9
Embodied world models predict the outcomes of robot actions to support learning and planning. For robots equipped with head and wrist cameras, this requires complementary views: th…
SafeAtlas-VL: Beyond Binary Multimodal Safety with Large-Scale Data and Guard Models
Zongrui Wang, Xiangyang Zhu, Sicheng Wang +13
Multimodal safety moderation requires distinguishing risks arising from visual content, user intent, and assistant behavior. Existing safeguards, however, are typically trained for…
LLaVA-Assessor: Building the Foundation LMM For Visual Quality Assessment
Ziheng Jia, Zicheng Zhang, Jiaying Qian +2
Aligning with the human visual system~(HVS) in perceiving and evaluating the quality of visual signals is a central objective of machine-vision-based visual quality assessment syst…
TREND-10K: A Comprehensive Dataset for Next-Generation Video Quality Assessment Based on Preference-Driven Media
Ziheng Jia, Zicheng Zhang, Junqi Zhang +4
The increasing prominence of short-video platforms, coupled with the advanced commercialization of AI-generated content (AIGC) videos, has led to a shift in the types of video medi…
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
Yilin Jiang, Xiaorong Zhu, Fei Tan +9
Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators. These roles place demands that ordinary question answering does…
RAVEN-Eval: Rubric-Guided Automatic Evaluation for AI Video Generation Models Based on LMM Preference Judgement
Ziheng Jia, Jiaying Qian, Zicheng Zhang +3
AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-art AI video generation model…
FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models
Zhida Jiang, Zhaolong Xing, Yang Pei +14
Industrial-grade distributed training of vision-language models (VLMs) remains far less efficient than that of unimodal LLMs. Existing solutions either follow a monolithic design t…
BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training
Jiaxing Wang, Deping Xiang, Jin Xu +9
As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive lear…
TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
Jiaxing Wang, Deping Xiang, Jin Xu +9
The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-…
Towards Characterizing Scientific Image Utility and Upgradability
WenZhe Li, Qihang Yan, Liang Chen +6
Scientific images function as critical evidence in research communication, yet their integrity faces unprecedented threats from AI-generated content that introduces subtle but cons…
DyCoRM: Dynamic Criterion-Aware Reward Modeling for Text-to-Image Generation
Jiaying Qian, Ziheng Jia, Qian Zhang +5
With the continued advancement of text-to-image (T2I) generation, producing high-quality images is becoming increasingly attainable; consequently, user demands are shifting toward…
GeoR-Bench: Evaluating Geoscience Visual Reasoning
Yushuo Zheng, Zicheng Zhang, Huiyu Duan +7
Geoscience intelligence is expected to understand, reason about, and predict earth system changes to support human decision-making in critical domains such as disaster response, cl…
MirrorBench: Evaluating Self-centric Intelligence in MLLMs by Introducing a Mirror
Shengyu Guo, Tongrui Ye, Jianbo Zhang +3
Recent progress in Multimodal Large Language Models (MLLMs) has demonstrated remarkable advances in perception and reasoning, suggesting their potential for embodied intelligence.…
Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition
Yushuo Zheng, Huiyu Duan, Zicheng Zhang +3
The ability of large language models (LLMs) to manage and acquire economic resources remains unclear. In this paper, we introduce \textbf{Market-Bench}, a comprehensive benchmark t…
SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond
Xiangyang Zhu, Yuan Tian, Qi Jia +14
The success of large language models (LLMs) in scientific domains has heightened safety concerns, prompting numerous benchmarks to evaluate their scientific safety. Existing benchm…
A: Towards Advertising Aesthetic Assessment
Kaiyuan Ji, Yixuan Gao, Lu Sun +7
Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized…
Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning
Yushuo Zheng, Huiyu Duan, Zicheng Zhang +2
Geolocation, the task of identifying the geographic location of an image, requires abundant world knowledge and complex reasoning abilities. Though advanced large multimodal models…
SIQA: Toward Reliable Scientific Image Quality Assessment
Wenzhe Li, Liang Chen, Junying Wang +6
Scientific images fundamentally differ from natural and AI-generated images in that they encode structured domain knowledge rather than merely depict visual scenes. Assessing their…
UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities
Qi Jia, Haodong Zhao, Dun Pei +7
Benchmarking large language models (LLMs) and agents in multi-turn interactive scenarios is essential for understanding their practical capabilities. However, existing evaluation p…
STAR : Bridging Statistical and Agentic Reasoning for Large Model Performance Prediction
Xiaoxiao Wang, Chunxiao Li, Junying Wang +6
As comprehensive large model evaluation becomes prohibitively expensive, predicting model performance from limited observations has become essential. However, existing statistical…
RISE-Video: Can Video Generators Decode Implicit World Rules?
Mingxin Liu, Shuran Ma, Shibei Meng +9
While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored fro…
VideoAesBench: Benchmarking the Video Aesthetics Perception Capabilities of Large Multimodal Models
Yunhao Li, Sijing Wu, Zhilin Gao +5
Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual perception tasks, which has in turn made the evaluation of LMMs significant. However, th…
Automated Safety Benchmarking: A Multi-agent Pipeline for LVLMs
Xiangyang Zhu, Yuan Tian, Zicheng Zhang +6
Large vision-language models (LVLMs) exhibit remarkable capabilities in cross-modal tasks but face significant safety challenges, which undermine their reliability in real-world ap…
Q-Bench-Portrait: Benchmarking Multimodal Large Language Models on Portrait Image Quality Perception
Sijing Wu, Yunhao Li, Zicheng Zhang +5
Recent advances in multimodal large language models (MLLMs) have demonstrated impressive performance on existing low-level vision benchmarks, which primarily focus on generic image…