Decision-Centric Fusion: A Unified Decision Variable for Modality Alignment and Task Adaptation

Infrared and visible image fusion is fundamentally limited by modality imbalance, where visible textures degrade under adverse conditions while infrared remains stable. Existing methods address this by optimizing encoding, fusion, and refinement as independent modules, yet they lack a shared degradation consensus, causing information asymmetry that undermines fusion quality. We propose Decision-Centric Fusion, which introduces a unified decision variable as the global decision signal, enabling all modules to optimize within the same degradation-aware space. The framework comprises three native components. The System Decision Module generates the unified variable to guide the full pipeline. Building on this, the Zip-Alignment Module aligns the two modalities through four interlocking gears: encoder attention alignment with infrared high-frequency injection, adaptive rank decomposition, soft query replacement, and decision-guided fusion weighting. The Boundary-Aware Refiner then identifies a safe operating region, ensuring that post-processing improves perceptual quality without degrading task performance. To facilitate deployment, knowledge distillation compresses the pipeline to 0.25M parameters with negligible loss. Experiments on four benchmarks validate the effectiveness, efficiency, and boundary behavior of the proposed framework.