Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios.
arXiv:2602. 17394v2 Announce Type: replace-cross Abstract: Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, flexible, and resilient communications in environments where terrestrial infrastructure is degraded or unavailable.
By Nuno Saavedra, Pedro Ribeiro, Andr\'e Coelho, Rui Campos
arXiv:2609.35821v1 Announce Type: new
Abstract: Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian needs, but generative artificial in...
By Xiaoshan Zhou, Zaifu Zhan
The paper presents an LLM-based framework for automatically classifying crisis levels in psychological support hotlines, addressing variability in human judgments and staffing constraints. It introduces a paralinguistic injection method that embeds non‑verbal emotional cues into transcripts, allowing the model to consider acoustic nuances. A reasoning‑enhanced training strategy encourages the model to produce diagnostic reasoning chains, which regularizes and improves classification, achieving a macro F1‑score of 0.802 and accuracy of 0.805 in 5‑fold cross‑validation.
By Terumi Chiba, Yang Luo, Ziyun Cui, Yongsheng Tong, Chao Zhang
arXiv:2607. 22692v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support despite lacking mechanisms to safely govern evolving mental health risk.
By Anabela C. Areias, Catarina Botelho, Ant\'onio Farinhas, Areti Vassilopoulos, Dora Janela, Xin Tong, Nuno M. Guerreiro, Maya D'Eon, Fab\'iola Costa, Ricardo Rei
The paper introduces a two‑stage training framework that combines Supervised Fine‑Tuning (SFT) and Direct Preference Optimization (DPO) to improve multimodal disaster severity assessment. It creates two datasets—ReasoningSet for validated rationales and PreferenceSet for paired rationales—using a single Human‑in‑the‑Loop workflow. Experiments on InternVL‑3‑8B and LLaVA‑1.5‑7B show that SFT boosts classification accuracy and Macro‑F1, while DPO further enhances interpretability and alignment with human judgment.
By Yuanjun Zhang, Fuzel Ahamed Shaik, Suvojit Acharjee, Fahad Khalid, Mourad Oussalah
arXiv:2606. 06217v1 Announce Type: cross Abstract: When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude UAV views and under tight on-site compute constraints.
By Tan Zhang, Quanyou Li, Lu Zhang, Jun Liu, Xiaofeng Zhu, Ping Hu
Anian is a safety‑gated multimodal AI backend designed for perinatal mental‑health support and mindfulness‑intervention routing. It maps user input into a four‑layer hierarchical state representation—emotion, psychosocial constructs, safety risk, and intervention routes—then fuses local and external risk signals to decide whether to generate AI responses or provide fixed safety content. Prototype evaluation on large public corpora showed high classification performance and perfect high‑risk recall in a controlled stress test, though clinical validity remains unestablished.
By Lei Wang, Xiao Wang, Lei Li
arXiv:2604. 00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions.
By Anurag Kumar, Raghuveer Peri, Jon Burnsky, Alexandru Nelus, Rohit Paturi, Srikanth Vishnubhotla, Yanjun Qi
arXiv:2607. 08625v1 Announce Type: new Abstract: Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment.
By Jo\~ao Matos, Olivia Buege, Donny Cheung, Gary S. Collins, Paula Dhiman, Nan Li, Bingyu Mao, Benjamin W. Nelson, Michail Ouroutzoglou, Paul Varghese, Jonathan Amar
arXiv:2609.20850v1 Announce Type: new
Abstract: While Multimodal Large Language Models (MLLMs) show remarkable advancements, their cross-modal capabilities introduce complex vulnerabilities that easi...
By Yueming Lyu, Yilian Shi, Haoxiang Tan, Linzhuang Zou, Qihao Wang, Guihua Yu, Jie Qin, Xin Gao, Chenyang Si, Jing Dong, Caifeng Shan
The paper introduces BEACON, a multilingual agent system designed to deliver personalized wildfire evacuation guidance to users, including navigation routes, checklists, and a chatbot that adapts to the user’s language. It integrates real‑time fire perimeter, evacuation orders, shelter data, GPS, and NOAA weather to predict fire danger using an XGBoost model and triggers alerts with polygon‑avoidant routing when danger is likely. The interface automatically switches languages based on the user’s recent settings or chatbot interactions, ensuring inclusive communication for non‑English speakers.
By Shruti Kulkarni, Lynn Tong, Aditi Namboodiripad, Chelyah Miller, Helen Lin, Peeyush Patel, Bogdan Bistriceanu, Diane Myung-kyung Woodbridge