VCA: Video Curious Agent for Long Video Understanding
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Long video understanding poses unique challenges due to their temporal complexity and low information density. Recent works address this task by sampling numerous frames or incorporating auxiliary tools using LLMs, both of which result in high computational costs. In this work, we introduce a curiosity-driven video agent with self-exploration capability, dubbed as VCA. Built upon VLMs, VCA autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences. Instead of directly sampling frames, VCA employs a tree-search structure to explore video segments and collect frames. Rather than relying on external feedback or reward, VCA leverages VLM's self-generated intrinsic reward to guide its exploration, enabling it to capture the most crucial information for reasoning. Experimental results on multiple long video benchmarks demonstrate our approach's superior effectiveness and efficiency.
Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 66 of its 80 references that are in the map. First posted on arXiv 12 Dec 2024.
Builds on
66 of 80 references.
Closest ancestors · 3
Same direction, newest first
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- 2024VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding18 Mar
- 2024VideoAgent: Long-form Video Understanding with Large Language Model as Agent15 Mar
Other references · 63
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Citing papers per year: 2025: 21; 2026: 33.
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