10 papers
Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper
Rongjin Li, Yuanxin Liu, Hao Zhou +3
Multimodal large language models (MLLMs) are increasingly capable scientific assistants, yet they remain far from fully autonomous research. This transition requires models to acti…
Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence
Kun Ouyang, Yuanxin Liu, Linli Yao +5
Video reasoning, which requires multi-step deduction across frames, remains a major challenge for multimodal large language models (MLLMs). While reinforcement learning (RL)-based…
RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction
Yuchi Wang, Yishuo Cai, Shuhuai Ren +6
Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimo…
SpaceR: Reinforcing MLLMs in Video Spatial Reasoning
Kun Ouyang, Yuanxin Liu, Haoning Wu +5
Video spatial reasoning, which involves inferring the underlying spatial structure from observed video frames, poses a significant challenge for existing Multimodal Large Language…
VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video Reasoning?
Yuanxin Liu, Kun Ouyang, Haoning Wu +7
Recent studies have shown that long chain-of-thought (CoT) reasoning can significantly enhance the performance of large language models (LLMs) on complex tasks. However, this benef…
TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
Linli Yao, Yicheng Li, Yuancheng Wei +11
The rapid growth of online video platforms, particularly live streaming services, has created an urgent need for real-time video understanding systems. These systems must process c…
TEMPLE: Incentivizing Temporal Understanding of Video Large Language Models via Progressive Pre-SFT Alignment
Shicheng Li, Lei Li, Kun Ouyang +7
Video Large Language Models (Video LLMs) have achieved significant success by adopting the paradigm of large-scale pre-training followed by supervised fine-tuning (SFT). However, e…
UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?
Yuanxin Liu, Rui Zhu, Shuhuai Ren +4
With the rapid growth of video generative models (VGMs), it is essential to develop reliable and comprehensive automatic metrics for AI-generated videos (AIGVs). Existing methods e…
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension
Kun Ouyang, Yuanxin Liu, Shicheng Li +5
Multimodal punchlines, which involve humor or sarcasm conveyed in image-caption pairs, are a popular way of communication on online multimedia platforms. With the rapid development…
Temporal Reasoning Transfer from Text to Video
Lei Li, Yuanxin Liu, Linli Yao +6
Video Large Language Models (Video LLMs) have shown promising capabilities in video comprehension, yet they struggle with tracking temporal changes and reasoning about temporal rel…