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Sensing User Intent: An LLM-Powered Agent for On-the-Fly Personalized Virtual Space Construction from UAV Sensor

Sanbi Luo1

  • 1Software Engineering Center, Chinese Academy of Sciences, Beijing 100190, China.

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Summary

CurationAgent enhances ecological data interpretation using Unmanned Aerial Vehicle (UAV) sensor data. This intelligent system provides more responsive, reliable, and precise public experiences compared to existing solutions.

Keywords:
LLM agentsUAV sensor datadynamic content generationhybrid IDC-RAGuser intent sensing

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Area of Science:

  • Ecological informatics
  • Human-computer interaction
  • Artificial intelligence

Background:

  • Unmanned Aerial Vehicles (UAVs) generate vast ecological data, but public engagement remains a challenge.
  • Current methods like static displays and general Large Language Model (LLM) agents lack adaptability and reliability.
  • Bridging the gap between raw sensor data and personalized public experiences requires novel intelligent systems.

Purpose of the Study:

  • To introduce CurationAgent, an intelligent agent designed for curating personalized public experiences from UAV-derived ecological data.
  • To address limitations in responsiveness, reliability, and precision found in existing data interpretation solutions.
  • To validate a new paradigm for interactive systems focused on partnered experience curation.

Main Methods:

  • Developed CurationAgent using the State-Gated Agent Architecture (SGAA).
  • Implemented a hybrid curation pipeline combining Retrieval-Augmented Generation (RAG) and an Intent-Driven Curation (IDC) Funnel.
  • Validated the system through a virtual exhibition of Lalu Wetland biodiversity and a user study.

Main Results:

  • CurationAgent demonstrated superior performance: 1512 ms response time (vs. 4301 ms), 95% task success (vs. 57%), and 85.5% query precision (vs. 52.7%).
  • A user study with 27 participants confirmed significantly higher user engagement.
  • The hybrid RAG and IDC approach effectively translated user intent into curated, multi-modal narratives.

Conclusions:

  • CurationAgent offers a robust and responsive solution for curating personalized ecological data experiences.
  • The State-Gated Agent Architecture (SGAA) provides a foundation for advanced interactive systems.
  • This work establishes a new paradigm for interactive systems, moving towards active, partnered experience curation from dynamic sensor data.