2 papers
cs.LG2026
ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking
Lequan Lin, Dai Shi, Andi Han +7
Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large lan…
cs.CV2025
Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning
Haoji Zhang, Xin Gu, Jiawen Li +7
The video reasoning ability of multimodal large language models (MLLMs) is crucial for downstream tasks like video question answering and temporal grounding. While recent approache…