Zhao-Yang Wei

Seeing is Believing: Grounding Long-Video Understanding in Spatio-Temporal Visual Evidence

DraftMind: Efficient Video Understanding in Vision-Language Models via Chain of Draft

Large vision-language models (LVLMs) such as Qwen-VL series have demonstrated strong capabilities in visual understanding and reasoning, particularly for static images. However, extending them to video reasoning remains computationally expensive, often incurring substantial latency and token usage under conventional Ch...

CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression

Visual latent reasoning compresses rendered derivations into compact intermediate states, reducing textual reasoning overhead. Existing approaches differ in how they represent these states: continuous methods avoid vocabulary constraints, whereas discrete methods improve accuracy through quantization into a finite code...

DraftMind: Efficient Video Understanding in Vision-Language Models via Chain of Draft

Large vision-language models (LVLMs) such as Qwen-VL series have demonstrated strong capabilities in visual understanding and reasoning, particularly for static images. However, extending them to video reasoning remains computationally expensive, often incurring substantial latency and token usage under conventional Ch...

Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?

Video-HolmesV2 is presented, a novel benchmark designed for Deep Audio-Visual Coupling that enforces an Evidence-Based Evaluation, requiring models to justify answers with precise spatio-temporal audio-visual evidence, thereby reducing confounding effects of guessing and hallucinated evidence.

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

Evidence-grounded visual reasoning (EGVOR) is proposed, departing from implicit inference, EGVOR reformulates reasoning as the explicit generation of Evidence Atoms–structured triplets that enforce strict spatial-semantic alignment.

LUT: Latent Utility Training for Visual Reasoning

Experiments on perception-intensive visual reasoning benchmarks show that LUT outperforms previous latent reasoning methods and remains competitive with latent-text interleaved methods with lower annotation cost.