Explainable Multimodal Models for Critical Infrastructure

Critical infrastructure operators increasingly rely on multimodal perception systems that fuse imagery, acoustic signatures, and telemetry feeds. Unfortunately, explainability research has lagged the architectural complexity of these systems. I propose a governance framework that blends modal-specific rationales with a global semantic narrative aligned to operator workflows. The pipeline begins with disentangled encoders whose latent spaces are regularised to preserve modality provenance. During inference, each encoder emits a sparse explanation graph that ties salient observations back to physical phenomena, for example corrosion cues in thermal imagery or harmonic anomalies in vibration spectra.

These graphs are then composed using a probabilistic argumentation layer. Instead of concatenating feature attributions, the layer synthesises coherent narratives expressed as structured natural language statements. Anomaly triage teams can therefore interrogate hypotheses at the level of domain semantics rather than raw tensor contributions. To maintain fidelity, every narrative clause is linked to a replay buffer of ranked exemplars that demonstrate how similar evidence manifested historically. This traceability transforms explanations into actionable briefings that accelerate root cause analysis.

Our pilot deployment within an electrical grid control centre demonstrated measurable benefits. Mean time to resolution dropped by eighteen percent because engineers could cross-validate multimodal cues without leaving the incident console. Regulatory auditors also praised the clarity of the narrative logs, which satisfied transparency mandates without requiring additional documentation. The study confirms that explainability must evolve from isolated saliency heatmaps to layered storytelling tailored to the decision horizons of real-world operators.