1 citations · 2 across the 23 of their papers we have counts for
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PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction
Bohan Liang, Zijian Chen, Qi Jia +3
Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied. Current large language models (LLMs)…
LiveProteinBench: A Contamination-Free Benchmark for Assessing Models' Specialized Capabilities in Protein Science
Dingyi Rong, Zijian Chen, Qi Jia +4
In contrast to their remarkable performance on general knowledge QA, the true abilities of Large Language Models (LLMs) in tasks demanding deep, specialized reasoning, such as in p…
ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation
Zitong Xu, Huiyu Duan, Xiaoyu Wang +6
With the rapid advancement of generative models, powerful image editing methods now enable diverse and highly realistic image manipulations that far surpass traditional deepfake te…
One Battle After Another: Probing LLMs' Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework
Qi Jia, Ye Shen, Xiujie Song +5
Evaluating LLMs' instruction-following ability in multi-topic dialogues is essential yet challenging. Existing benchmarks are limited to a fixed number of turns, susceptible to sat…
VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results
Dasong Li, Sizhuo Ma, Hang Hua +40
This paper presents an overview of the VQualA 2025 Challenge on Engagement Prediction for Short Videos, held in conjunction with ICCV 2025. The challenge focuses on understanding a…
SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
Xiangyang Zhu, Yuan Tian, Chunyi Li +3
The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM s…
Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Kaiwei Zhang, Qi Jia, Zijian Chen +5
Large language models (LLMs), while increasingly used in domains requiring factual rigor, often display a troubling behavior: sycophancy, the tendency to align with user beliefs re…
User-centric Subjective Leaderboard by Customizable Reward Modeling
Qi Jia, Xiujie Song, Zicheng Zhang +4
Existing benchmarks for large language models (LLMs) predominantely focus on assessing their capabilities through verifiable tasks. Such objective and static benchmarks offer limit…
Can Large Models Fool the Eye? A New Turing Test for Biological Animation
Zijian Chen, Lirong Deng, Zhengyu Chen +5
Evaluating the abilities of large models and manifesting their gaps are challenging. Current benchmarks adopt either ground-truth-based score-form evaluation on static datasets or…
Static and Plugged: Make Embodied Evaluation Simple
Jiahao Xiao, Jianbo Zhang, BoWen Yan +9
Embodied intelligence is advancing rapidly, driving the need for efficient evaluation. Current benchmarks typically rely on interactive simulated environments or real-world setups,…
VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning
Linhan Cao, Wei Sun, Weixia Zhang +6
Video quality assessment (VQA) aims to objectively quantify perceptual quality degradation in alignment with human visual perception. Despite recent advances, existing VQA models s…
Generalizable Video Quality Assessment via Weak-to-Strong Learning
Linhan Cao, Wei Sun, Xiangyang Zhu +6
Video quality assessment (VQA) seeks to predict the perceptual quality of a video in alignment with human visual perception, serving as a fundamental tool for quantifying quality d…
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
Xin Li, Kun Yuan, Bingchen Li +110
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Qualit…