ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
UAVs meet LLMs: Overviews and perspectives towards agentic low-altitude mobility , volume=
4 Pith papers cite this work, alongside 49 external citations. Polarity classification is still indexing.
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FRAMe combines an LLM planner with RAG-based memory and a multi-modal coach agent to generate valid, preference-aligned eVTOL flight plans, achieving up to 93.8% validity across four LLMs.
RefGlass-GS is a fusion framework using UAV data, MAP-based panel segmentation, viewpoint optimization, and modified Gaussian Splatting with Reflection MLP to achieve improved photorealistic and semantic modeling of reflective glass facades.
BifrostRAG combines dual knowledge graphs with hybrid retrieval to improve multi-hop question answering on construction safety regulations, reporting 87.3% F1 on a custom dataset.
citing papers explorer
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ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
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End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
FRAMe combines an LLM planner with RAG-based memory and a multi-modal coach agent to generate valid, preference-aligned eVTOL flight plans, achieving up to 93.8% validity across four LLMs.
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RefGlass-GS: A UAV-Enabled Fusion Framework for Photorealistic, Semantic and Interactive Digitization of Reflective Glass Facades via Gaussian Splatting
RefGlass-GS is a fusion framework using UAV data, MAP-based panel segmentation, viewpoint optimization, and modified Gaussian Splatting with Reflection MLP to achieve improved photorealistic and semantic modeling of reflective glass facades.
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Bridging Dual Knowledge Graphs for Multi-Hop Question Answering in Construction Safety
BifrostRAG combines dual knowledge graphs with hybrid retrieval to improve multi-hop question answering on construction safety regulations, reporting 87.3% F1 on a custom dataset.