7 papers
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…
CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding
Wei Jia, Zhicong Lu, Yu Chen +6
Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL). However, existing methods pr…
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…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains
Garvin 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…
Beyond Visual Memory: Mechanistic Diagnostics of Latent Visual Reasoning
Garvin Guo, Yu Chen, Xiang Wang +4
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…