arXiv:2512.10359v1 Announce Type: cross Abstract: Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios. However, existing Multimodal Large Language Models (MLLMs) struggle with simultaneously modeling spatial relationships within video frames and understanding the causal dynamics of temporal evolution on complex and reasoning-intensive VideoQA task. In this work, we equip MLLM with a comprehensive and extensible Video Toolkit, to enhance MLLM's spatiot
The continuous evolution of MLLMs and the increasing complexity of real-world video data are pushing researchers to address limitations in spatiotemporal reasoning for advanced AI applications.
Improving spatiotemporal reasoning in MLLMs is crucial for developing more robust and reliable AI systems that can accurately perceive and interact with dynamic environments, moving beyond basic pattern recognition.
The integration of specialized toolkits with MLLMs will enhance their ability to process and understand complex video narratives, potentially improving performance in areas like robotics, surveillance, and content creation.
- · AI researchers
- · Video analytics companies
- · Developers of MLLMs
- · Autonomous systems
- · Legacy video processing methods
- · Simple MLLMs without tool integration
Enhanced MLLM capabilities for nuanced video understanding and question answering.
Accelerated development of more sophisticated AI agents capable of operating in complex, dynamic visual environments.
Potential for new human-computer interaction paradigms based on advanced video interpretation and causal reasoning.
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Read at arXiv cs.AI