"I believe the technology was deployed too quickly in too vast amounts, with hundreds of vehicles, when it wasn't really ready," one police official told federal regulators last month. Emergency first-responder leaders told federal regulators in a private meeting last month that they were frustrated with the performance of autonomous vehicles on their streets--that city firefighters, police officers, EMTs, and paramedics are forced to spend time during emergencies resolving issues with frozen or stuck cars. One fire official called them "a safety issue for our crews as well as the victims." WIRED obtained an audio recording of the meeting. Officials from San Francisco and Austin, where Waymo has been ferrying passengers without drivers for more than a year, said the vehicles' performance is getting worse.

We introduce the novel EAI framework for integrating emotion modeling into LLMs to examine the emotional impact on ethics and LLM-based decision-making in various strategic games, including bargaining and repeated games. Our experimental study with various LLMs demonstrated that emotions can significantly alter the ethical decision-making landscape of LLMs, highlighting the need for robust mechanisms to ensure consistent ethical standards. Our game-theoretic analysis revealed that LLMs are susceptible to emotional biases influenced by model size, alignment strategies, and primary pretraining language. Notably, these biases often diverge from typical human emotional responses, occasionally leading to unexpected drops in cooperation rates, even under positive emotional influence.

Constructing a directed cyclic graph (DCG) is challenged by both algorithmic difficulty and computational burden. Comparing multiple DCGs is even more difficult, compounded by the need to identify dynamic causalities across graphs.

Abstract-- This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (UA Vs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UA V identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-robot systems can augment human capabilities in disaster response scenarios to maximize lives saved. I. INTRODUCTION Robotics has long sought to augment human capabilities in hazardous scenarios. Mass-casualty incidents (MCIs), such as those resulting from natural disasters, bombings, plane crashes, or industrial chemical spills, present an opportunity for robotic systems to assist first responders. The critical first step of providing medical assistance during MCIs is primary triage: the initial process of locating victims at the site of the MCI and assessing the severity of their injuries to prioritize treatment, which is essential to optimizing survival outcomes. Traditionally, primary triage relies on human responders who may face significant risk and information overload [1], underscoring the potential for automated systems to mitigate these challenges. While prior efforts have explored the use of air-ground robotic teams for search and exploration in disaster zones [2]-[5], few systems have focused specifically on rapid triage. Existing approaches typically solve parts of the problem in isolation without integrating comprehensive triage functions. For example, air-ground teams have also been developed to find and localize objects of interest [3], [6] Authors are with the GRASP Lab, School of Engineering and Applied Sciences, University of Pennsylvania. Authors are with the Perelman School of Medicine, University of Pennsylvania. This work was supported by the DARP A Triage Challenge under grant #HR001123S0011.

This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enhanced SA. Results show that most FRs expressed positive attitudes toward robots, and rated the usefulness of semantic information for building SA at an average of 3.6 out of 5. Semantic information was also valued for its role in predicting unforeseen emergencies (mean 3.9). Participants reported requiring an average of 74.6\% accuracy to trust semantic outputs and 67.8\% for them to be considered useful, revealing a willingness to use imperfect but informative AI support tools. To the best of our knowledge, this study offers novel insights by being one of the first to directly survey FRs on semantic-based SA in a cross-national context. It reveals the types of semantic information most valued in the field, such as object identity, spatial relationships, and risk context-and connects these preferences to the respondents' roles, experience, and education levels. The findings also expose a critical gap between lab-based robotics capabilities and the realities of field deployment, highlighting the need for more meaningful collaboration between FRs and robotics researchers. These insights contribute to the development of more user-aligned and situationally aware robotic systems for emergency response.

A new pilot program aims to help EMS respond quicker, not act as a replacement. Breakthroughs, discoveries, and DIY tips sent every weekday. When they aren't baffling the public or grounding wildfire planes, drones have some pretty solid uses. Apart from unnecessarily fast same-day deliveries, the pilotless aircrafts may soon become a lifesaving emergency response tool . A collaborative team of health experts, community organizations, and universities are in the middle of a pilot program using drones and automated external defibrillators (AEDs).

Large language models (LLMs) increasingly mediate economic and organisational processes, from automated customer support and recruitment to investment advice and policy analysis. These systems are often assumed to embody rational decision making free from human error; yet they are trained on human language corpora that may embed cognitive and social biases. This study investigates whether advanced LLMs behave as rational agents or whether they reproduce human behavioural tendencies when faced with classic decision problems. Using two canonical experiments in behavioural economics, the ultimatum game and a gambling game, we elicit decisions from two state of the art models, Google Gemma7B and Qwen, under neutral and gender conditioned prompts. We estimate parameters of inequity aversion and loss-aversion and compare them with human benchmarks. The models display attenuated but persistent deviations from rationality, including moderate fairness concerns, mild loss aversion, and subtle gender conditioned differences.

Emergency responders managing hazardous material HAZMAT incidents face critical, time-sensitive decisions, manually navigating extensive chemical guidelines. We investigate whether today's language models can assist responders by rapidly and reliably understanding critical information, identifying hazards, and providing recommendations. We introduce the Chemical Emergency Response Evaluation Framework (ChEmREF), a new benchmark comprising questions on 1,035 HAZMAT chemicals from the Emergency Response Guidebook and the PubChem Database. ChEmREF is organized into three tasks: (1) translation of chemical representation between structured and unstructured forms (e.g., converting C2H6O to ethanol), (2) emergency response generation (e.g., recommending appropriate evacuation distances) and (3) domain knowledge question answering from chemical safety and certification exams. Our best evaluated models received an exact match of 68.0% on unstructured HAZMAT chemical representation translation, a LLM Judge score of 52.7% on incident response recommendations, and a multiple-choice accuracy of 63.9% on HAMZAT examinations. These findings suggest that while language models show potential to assist emergency responders in various tasks, they require careful human oversight due to their current limitations.