We study AstroCLIMB, a four-way relationship-classification task over pairs of astronomy figure/caption endpoints. Starting from a leakage-audited lexical and low-level image-statistics baseline (M7), we evaluate a hosted multimodal judge under a fixed, auditable protocol. GPT-5.6 Luna receives only the two endpoint contents and produces integer class probabilities for three views in both endpoint directions. On 2,000 frozen validation pairs, all 12,000 calls succeeded. Equal-weight fusion reaches macro-F1 0.67, versus M7’s 0.40 (paired component-bootstrap delta +0.27, 95% CI [+0.24,+0.29]); Luna-only reaches 0.68. These are conditional local-validation results, not an official Luna leaderboard score. We therefore report an auditable system study without claiming paper-level generalization.