Explaining Toxicity Emergence in Multimodal Foundation Models through Internal Computational Path Analysis
Keywords:
multimodal foundation models, toxicity, mechanistic interpretability, computational paths, model safety, interventionAbstract
The rapid deployment of multimodal foundation models across high-stakes sociotechnical systems has brought the challenge of toxicity emergence to the forefront of AI safety research. Unlike earlier unimodal systems, these models integrate text, vision, and other modalities to form joint representations, creating novel internal computational pathways through which toxic content can be generated or amplified. This paper presents a system-level framework for explaining toxicity emergence through internal computational path analysis, a methodological orientation rooted in mechanistic interpretability that traces how inputs, attention flows, and cross-modal interactions combine to produce harmful outputs. We argue that toxicity in multimodal foundations is not a static property of training data but an emergent phenomenon shaped by architectural design choices, training objectives, and the dynamic routing of information across modality-specific and cross-modal circuits. Drawing on recent advances in circuit analysis and representation editing, we examine the structural trade-offs inherent in scaling, fine-tuning, and alignment procedures that often mask but do not eliminate toxic predispositions. Through detailed conceptual analysis, case illustrations, and cross-domain comparisons, we critically assess the promise of path-level interventions as a robust safety mechanism. We further discuss ramifications for governance, robustness, fairness, and the sustainability of safety measures in production systems. The analysis situates computational path tracing as a necessary complement to surface-level filtering, offering a principled lens for auditing, repairing, and certifying large multimodal models before and after deployment.
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