Samir Baral

When natural disasters strike, emergency operations centers lose 45 critical minutes manually cross-referencing aerial drone footage, chaotic radio chatter, and static maps. Worse, off-the-shelf vision models routinely hallucinate evacuation routes straight through 12 feet of raging floodwater. CrisisGrid is an autonomous multimodal disaster triage, infrastructure assessment, and emergency dispatch agent built on 5 Meta frontier models with a non-negotiable Verifiable-Agent Principle: Neural models propose; pure deterministic Python mathematics disposes. CrisisGrid executes 9x parallel async sam-3.1 spatial segmentation calls to separate passable infrastructure from critical flood and debris hazards in 5.2 seconds. Simultaneously, muse-voice-transcribe-1.0 transcribes emergency responder storm maydays with over 99% accuracy. muse-spark-1.3 fuses the radio transcript with segmented aerial telemetry to evaluate urgency and propose candidate evacuation corridors. Crucially, every proposed corridor is mathematically audited by a deterministic Liang-Barsky geometry engine; any route intersecting floodwater past our 10% tolerance is instantly rejected with proof, forcing an autonomous self-correction loop onto dry elevated asphalt. Only mathematically verified coordinates reach muse-wonder-1.0 for high-effort tactical multi-unit dispatch (drones, swiftwater boats, medical staging). muse-image-1.0 renders an official tactical briefing card grounded strictly in verified state, and dispatchers can export standardized FEMA ICS-204 Incident Action Plans or stream live global seismic telemetry from the USGS feed.