Remote sensing MLLMs perform poorly on negation tasks with hallucinations and accuracy drops, but the NeFo test-time learning method substantially improves negation understanding and generalizes to unseen tasks using ~5% unlabeled test samples.
Georeason: Aligning thinking and answer- ing in remote sensing vision-language models via logi- cal consistency reinforcement learning
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RemoteShield improves robustness of Earth observation MLLMs by training on semantic equivalence clusters of clean and perturbed inputs via preference learning to maintain consistent reasoning under noise.
GeoDisaster provides a new benchmark for operational disaster geo-intelligence and proposes an RCEA-trained multi-agent framework with 18 geospatial tools that improves tool use and decision consistency over existing RS-VLMs.
citing papers explorer
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Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs
Remote sensing MLLMs perform poorly on negation tasks with hallucinations and accuracy drops, but the NeFo test-time learning method substantially improves negation understanding and generalizes to unseen tasks using ~5% unlabeled test samples.
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RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation
RemoteShield improves robustness of Earth observation MLLMs by training on semantic equivalence clusters of clean and perturbed inputs via preference learning to maintain consistent reasoning under noise.
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GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
GeoDisaster provides a new benchmark for operational disaster geo-intelligence and proposes an RCEA-trained multi-agent framework with 18 geospatial tools that improves tool use and decision consistency over existing RS-VLMs.