We present Cue3D, the first comprehensive, model-agnostic framework for quantifying the influence of individual image cues in single-image 3D generation.

We demonstrate scenarios where sparse attention based transformer models learn and generalize faster, and theoretically characterize conditions under which this occurs.

We provides the first systematic analysis of large reasoning model as translation evaluator and offer insights in improvements.