The WASP 2026 (AstroCLIMB) task requires classifying the relation between a pair of objects drawn from astronomy papers — same_figure, same_paper, related_papers, or unrelated_papers — where each pair combines text (captions) and/or images (figures) in one of three disjoint modality configurations: image+text, text+text, and image+image. Since same_figure cannot occur outside the image+text subset, a single unified classifier is structurally sub-optimal; we instead adopt three independent per-modality classifiers whose predictions are pooled for the competition metric (pooled macro-F1). Starting from a hand-engineered baseline (CLIP, SciNCL and DINOv2 embeddings with lexical/OCR features and a LightGBM ensemble), we find that replacing the encoder with a joint vision-language embedding model (Qwen3-VL-Embedding-2B) produces the largest single improvement among the experiments we tested (public leaderboard 0.4808 → 0.62319), exceeding the combined effect of six feature-engineering ideas that were implemented and measured but produced less than 0.01 macro-F1 each. We further show that zero-shot cross-encoder rerankers (Qwen3-VL-Reranker-2B, Qwen3-Reranker-4B), applied with no task-specific fine-tuning, add a second large improvement on top of the engineered ensemble (leaderboard 0.64210 → 0.66299), and that an untrained text+text reranker alone already outperforms the fully engineered pipeline on that subset (macro-F1 0.7111 vs. 0.6704). We report a parallel distant-supervision branch that trains on 120,000 externally-labeled pairs mined from the competition’s source corpus and reaches a comparable but lower leaderboard score (0.57713) through a different mechanism; under our tested data and modeling regimes, this suggests that representation quality was a stronger bottleneck than either additional hand-engineered features or additional distant supervision. All reported numbers are traced to raw out-of-fold prediction arrays or literal notebook output under a shared leak-free StratifiedKFold(5) protocol, and we document two negative results (a broken nested-honest validation split, and several feature-engineering ideas that measurably hurt) as concrete engineering findings.