Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts

Understanding convergent learning--the degree to which independently trained neural systems--whether multiple artificial networks or brains and models--arrive at similar internal representations--is crucial for both neuroscience and AI. Yet, the literature remains narrow in scope--typically examining just a handful of models with one dataset, relying on one alignment metric, and evaluating networks at a single post-training checkpoint. We present a large-scale audit of convergent learning, spanning dozens of vision models and thousands of layer-pair comparisons, to close these long-standing gaps.