ContextFireAgent: A Multi-Role Consensus Agent for RGB Wildfire Monitoring

Abstract

In real-world wildfire monitoring, haze, glare, refraction artifacts, and related visual effects can cause false alarms and missed detections. This study explores two implementations: ContextFireVLM and ContextFireAgent. ContextFireVLM fine-tunes Llama-3.2 Vision with LoRA to improve the base model’s ability to identify and analyze wildfires; ContextFireAgent equips an agent with YOLO as a tool to improve the same capabilities. The first and second assessors independently inspect the full image, and the second assessor invokes YOLO only when it reports uncertainty; when their judgments agree, the final reviewer preserves the agreed decision. Because the two implementations use different models, inputs, and training pipelines, these results do not establish a causal comparison or general superiority between LoRA adaptation and the agent design.