AgentVIF: Event-Aware Multi-Agent Adaptation to Dynamic Degradations in Visible–Infrared Video Fusion

Visible-infrared video fusion aims to integrate complementary texture and thermal information into temporally consistent fused videos. However, existing methods commonly rely on fixed fusion parameters and struggle with dynamic degradations whose types and severities vary over time. We propose an event-aware multi-agent framework that couples temporal degradation perception with parameter-space expert composition. A Sentinel Agent localizes degradation transitions, while a customized Expert Agent predicts continuous multi-label degradation severities from dual-modal bursts. These predictions guide a Fusion Mixer to adaptively merge degradation-specific task vectors and generate segment-specific fusion parameters. A Feedback Agent further detects local fusion failures and triggers re-diagnosis and re-fusion. We additionally construct a 20K dual-modal burst-level instruction dataset to customize the Expert Agent. Extensive experiments demonstrate improved fusion quality, temporal consistency, and downstream tracking performance across diverse video conditions.