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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2 Pith papers cite this work, alongside 1,645 external citations. Polarity classification is still indexing.
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Reasoning-tuned LLMs reliably complete navigation in partial-observability gridworlds but take longer paths than oracle optima, with few-shot prompting reducing invalid moves and action priors like UP/RIGHT causing loops.
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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LLMs for Text-Based Exploration and Navigation Under Partial Observability
Reasoning-tuned LLMs reliably complete navigation in partial-observability gridworlds but take longer paths than oracle optima, with few-shot prompting reducing invalid moves and action priors like UP/RIGHT causing loops.