10 papers
Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs
Xinming Wang, Weinong Wang, Hongming Yang +13
Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thinking inference. Although thes…
Claim-Level Rubric Rewards for Video Caption Reinforcement Learning
Mingqi Gao, Hongyuan Dong, Yifei Chen +6
In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinforcement learning for dense vi…
WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality
Chunyang Li, Yilun Zheng, Xinting Huang +5
The paradigm of LLM-as-a-judge is emerging as a scalable and efficient alternative to human evaluation, demonstrating strong performance on well-defined tasks. However, its reliabi…
GenArena: How Can We Achieve Human-Aligned Evaluation for Visual Generation Tasks?
Ruihang Li, Leigang Qu, Jingxu Zhang +6
The rapid advancement of visual generation models has outpaced traditional evaluation approaches, necessitating the adoption of Vision-Language Models as surrogate judges. In this…
Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies
Wenjin Hou, Wei Liu, Han Hu +3
Multimodal Large Language Models (MLLMs) have shown remarkable proficiency on general-purpose vision-language benchmarks, reaching or even exceeding human-level performance. Howeve…
Focal-RegionFace: Generating Fine-Grained Multi-attribute Descriptions for Arbitrarily Selected Face Focal Regions
Kaiwen Zheng, Junchen Fu, Songpei Xu +4
In this paper, we introduce an underexplored problem in facial analysis: generating and recognizing multi-attribute natural language descriptions, containing facial action units (A…