Testing across more architectures helps to clarify if ACD uncovers failures tied to model scale, training data, or architectural differences like MoE or other d

Testing across more architectures helps to clarify if ACD uncovers failures tied to model scale, training data, or architectural differences like MoE or other desings. If failures are model-specific quirks or can generalize across LLMs, that would support claims about ACD’s robustness and usefulness for broad AI evaluation.