This work presents a unified multimodal AI system for damage assessment that integrates retrieval-augmented generation (RAG) models, thermal spectrum perception, vision foundation model pipelines, and exploratory wireless signal sensing. A RAG component is developed to ground a locally hosted language model in project-specific documentation, including specialized damage level classification criteria to mitigate hallucinations during inference.
arXiv:2606. 17020v1 Announce Type: cross Abstract: Remote sensing vision-language models have advanced Earth observation understanding, but most existing work remains centered on RGB imagery, leaving the complementary information in infrared data underexplored.
By Jiaju Han, Ben Zhang, Xuemeng Sun, Qike Zhang, Yuxian Dong, Chengyin Hu, Fengyu Zhang, Yiwei Wei, Jiujiang Guo
arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2608. 09230v1 Announce Type: new Abstract: Industrial-safety understanding requires more than detecting workers, equipment, and personal protective equipment.
By Yuanchi Zhu, Kang An, Tengyue Wang, Zhongyu Yang, Chenxu Du, Xinqi Yang, Hebao Zhu, Bokai Zhao, Tianyu Liang, Ziliang Wang, Faqiang Qian, Yunli Yang, Weiyang Shi, Qibing Ren
Remote sensing vision-language models have advanced Earth observation understanding, but most existing work remains centered on RGB imagery, leaving the complementary information in infrared data underexplored. Infrared images provide distinctive cues, including thermal intensity structures, object boundaries, and illumination-invariant scene features, which can enrich visual-language learning beyond conventional RGB observations.
arXiv:2606. 15782v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response generation.
By Pratheswaran Hariharan, Haiping Xu, Donghui Yan
Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response generation. However, these systems can still produce overconfident predictions and hallucination-like outputs, particularly when the visual evidence is weak, ambiguous, or semantically inconsistent.
arXiv:2607. 21155v1 Announce Type: cross Abstract: Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiring external information beyond a provided image to answer questions.
By Hanseok Oh, Parishad BehnamGhader, Benno Krojer, Hyunji Lee, Paul Liang, Siva Reddy, Verna Dankers
arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.
By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni
arXiv:2607. 22746v1 Announce Type: cross Abstract: Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed.
By Hongruixuan Chen, He Huang, Haifeng Wang, Jian Song, Junjue Wang, Weihao Xuan, Hamish Mitchell, Jiepan Li, Wei He, Liangpei Zhang, Zijie Wang, Chen Zhong, Jiazhen Zhao, Lei Hu, Ting Hu, Hongyan Zhang, Gregory Angelides, Miriam Cha, Clifford Broni-Bediako, Junshi Xia, Taylor Perron, Naoto Yokoya
Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiring external information beyond a provided image to answer questions. KI-VQA involves multiple sub-problems -referring expression understanding, visual grounding, object recognition, knowledge retrieval, and reasoning-yet existing benchmarks typically report only end-task accuracy, obscuring where failures arise.
arXiv:2507. 20804v3 Announce Type: replace Abstract: Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge.
By Xueyao Wan, Hang Yu