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VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking
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VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking

#VideoSeek #long-horizon #video agent #tool-guided seeking #video understanding #AI #multimodal

📌 Key Takeaways

  • VideoSeek is a new AI agent designed for long-horizon video understanding.
  • It uses tool-guided seeking to efficiently navigate and analyze extended video content.
  • The approach aims to improve performance on complex, multi-step video tasks.
  • It represents an advancement in video AI by integrating external tools for enhanced reasoning.

📖 Full Retelling

arXiv:2603.20185v1 Announce Type: cross Abstract: Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively parsing the full video. This insight allows the model to use far fewer frames while mainta

🏷️ Themes

Video AI, Tool Integration

📚 Related People & Topics

Artificial intelligence

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Intelligence of machines

# Artificial Intelligence (AI) **Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...

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🏢 OpenAI 14 shared
🌐 Reinforcement learning 4 shared
🏢 Anthropic 4 shared
🌐 Large language model 3 shared
🏢 Nvidia 3 shared
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Artificial intelligence

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Why It Matters

This development matters because it represents a significant advancement in AI's ability to understand and navigate long-form video content, which has been a persistent challenge in computer vision. It affects video content creators, researchers analyzing surveillance or medical footage, and platforms needing to index hours of video efficiently. The tool-guided seeking approach could revolutionize how we interact with video archives, making previously unwieldy long-form content instantly searchable and analyzable.

Context & Background

  • Previous video AI systems have struggled with 'long-horizon' tasks requiring understanding of events spanning minutes or hours in video
  • Most existing video understanding models focus on short clips (seconds to minutes) due to computational constraints
  • The field has seen growing interest in applications like surveillance analysis, medical procedure review, and educational content navigation
  • Tool-use in AI agents has become increasingly important for complex reasoning tasks across domains

What Happens Next

Researchers will likely benchmark VideoSeek against existing video understanding systems and publish detailed performance metrics. The approach may be integrated into video analysis platforms within 6-12 months, with potential applications in content moderation, educational technology, and automated video summarization. Further development will focus on improving accuracy across diverse video types and reducing computational requirements.

Frequently Asked Questions

What makes VideoSeek different from other video AI systems?

VideoSeek specifically addresses 'long-horizon' video understanding through tool-guided seeking, allowing it to efficiently navigate and comprehend events spanning extended timeframes that overwhelm traditional systems.

What practical applications could this technology have?

Potential applications include automated video surveillance analysis, medical procedure review systems, educational content navigation tools, and enhanced video search capabilities for content platforms and archives.

How does the tool-guided seeking approach work?

The system uses specialized tools to strategically seek through video content rather than processing everything linearly, enabling efficient analysis of long videos by focusing computational resources on relevant segments.

What are the main limitations of current video AI that VideoSeek addresses?

Current systems struggle with computational constraints when processing long videos and often miss contextual relationships between distant events, which VideoSeek's seeking approach aims to overcome.

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Original Source
arXiv:2603.20185v1 Announce Type: cross Abstract: Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively parsing the full video. This insight allows the model to use far fewer frames while mainta
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Source

arxiv.org

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