A three-agent mobile system for end-to-end walking support shows motivational companion dialogue boosts affect and UX in a 12-person in-the-wild crossover study.
arXiv preprint arXiv:2509.05933 , year=
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The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.
citing papers explorer
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SmartWalkCoach: An AI Companion for End-to-End Walking Guidance, Motivation, and Reflection
A three-agent mobile system for end-to-end walking support shows motivational companion dialogue boosts affect and UX in a 12-person in-the-wild crossover study.
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Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents
The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.