We study whether knowledge-gating benchmarks support transferable retrieval decisions or expose benchmark-specific structural regularities that can be exploited without full semantic interpretation. Knowledge gating is the binary decision of whether a dialogue system should retrieve external knowledge at a given turn. We audit this question with a lightweight logistic-regression probe over ten dialogue-metadata features: position, length, and question-form cues. Despite using only metadata and surface signals, the probe reveals non-trivial in-domain signal and markedly different structural patterns across benchmark families. Cross-benchmark transfer provides the clearest evidence: transfer within the related DSTC9/DSTC11 family is near-lossless (Macro F1 0.81–0.84; ROC-AUC ≈ 0.90), whereas transfer across the KETOD–DSTC benchmark-family boundary has weak ranking quality (ROC-AUC 0.41–0.48) and poor detection of knowledge-needed turns (positive-class F1 ≤ 0.22 in one direction). Sentence embeddings and fine-tuned BERT show the same qualitative weakness in the DSTC→KETOD setting: MiniLM ranking is sub-chance (ROC-AUC 0.40–0.44), while BERT remains near chance (0.50–0.53) despite having the strongest KETOD in-domain positive F1. These results suggest that strong in-domain gating scores can partly reflect benchmark-specific regularities and should not, by themselves, be interpreted as evidence of transferable retrieval reasoning. Code and reproducibility materials are available at https://github.com/zilongcheng582-cyber/RAGate-analysis.