most citedBeyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs

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cs.CV2026

ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images

Shuhan Xia, Yu Chen, Xiang Wang +3

The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic and comprehensive perceptual asse…

cs.CV2026

RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling

Zhenchen Tang, Yang Li, Songlin Yang +6

Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often…

cs.CV2026

One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding

Wang Chen, Yu Chen, Xiang Wang +3

Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows. Since the appropriate frame budg…

cs.CV2026

Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception

Xinheng Han, Jianfei Wang, Yu Chen +4

Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-object binding, object cardinal…

cs.CV2026

Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains

Jiawei Guo, Donglei Yu, Yu Chen +6

Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interpretation can be premature: a t…

cs.CV2026

Beyond Visual Memory: Mechanistic Diagnostics of Latent Visual Reasoning

Jiawei Guo, Yu Chen, Xiang Wang +6

Recent latent visual reasoning methods achieve substantial gains by inserting continuous latent tokens into multimodal language models. These gains are commonly attributed to the t…

cs.CV2026

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

Zixian Su, Hongkai Zhang, Fan Gao +12

Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on…

cs.CV2026

EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation

Songlin Yang, Haobin Zhong, Ruilin Zhang +23

The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community tr…

cs.CV2026

Latent Action Control for Reasoning-Guided Unified Image Generation

Fuxiang Zhai, Sixiang Chen, Yingjin Li +4

Unified multimodal models can encode visual understanding and image generation within a shared backbone, yet understanding does not automatically translate into control: models may…