Abstract
<jats:p>Deep learning models for tabular data remain difficult to interpret, particularly when their internal dynamics are unstable or sensitive to noise. This study introduces a multimodal diagnostic overlay framework that analyses model behaviour across fractal, spectral, topological, reflex‑aware, and auditory modalities. Unlike post hoc attribution methods that assume stable gradients, the framework captures evolving internal structure during training, revealing instability regimes, structural transitions, and optimiser–noise interactions that conventional eXplainable Artificial Intelligence (XAI) techniques overlook. The approach is evaluated on multilayer perceptron (MLP) and gated recurrent unit (GRU) architectures to contrast memoryless and state‑dependent dynamics. The overlays expose gradient‑noise turbulence, fractal and topological surges, attribution drift, blind‑spot intervals, and reflex‑misalignment events, demonstrating that interpretability is inseparable from training behaviour. Results show that the GRU undergoes frequent re-organisations and collapse events, whereas the MLP remains comparatively stable, enabling more reliable explanations. The contributions include: (1) a diagnostic architecture for dynamic interpretability, (2) evidence that multimodal agreement and divergence illuminate mechanisms underlying instability, and (3) reflex‑responsiveness analysis as a novel interpretive signal. An ablation study confirms that each modality provides a distinct and necessary contribution. The findings motivate a shift from static, post hoc explanations towards behaviour‑aware interpretability with implications for responsible AI, model governance, and real‑time diagnostic monitoring.</jats:p>