4 accepted papers
Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform
Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform
Machine learning models for steel property prediction routinely report high quality metrics with $R^2\!>\!0.85$, yet these results rely on random splits that allow similar grades in both train and test sets. We present SteelAgent, an interactive system that exposes a critical generalization gap: the
Cities deploying autonomous vehicles face an urgent policy question: would the adoption of autonomous vehicles (AVs) improve the congestion rate or worsen it? What would be the optimal adoption rate to minimize the congestion rate? How would cautious AVs (Waymo-style) and aggressive AVs (Tesla "Mad