Arabic stance depends on quotation, sarcasm, negation, hashtags, and target scope, which surface polarity may miss. For StanceEval-2026, we study Codex and Gemini prompting through two submission systems: a replicated contextual Codex predictor and a gated judge that retrieves labeled examples using seven AI-generated discourse cues. On 619 Dev examples, cue retrieval changes Favg2 from 0.8580 to 0.8613 for GPT-5.6-luna, 0.8767 to 0.8844 for Gemini 3.5 Flash, and 0.8646 to 0.8554 for Flash-Lite. Random and semantic controls show no consistent retrieval advantage. On the 352-row hidden set, the best retained Codex member scores 0.8685 Favg2 and currently ranks fourth, while gated retrieval scores 0.8604. Codex and OPUS-MT translation pipelines score 0.7848 and 0.7304, and a synthetic-data classifier scores 0.7334. These results support replication and retrieval as auditable system components, not as general performance gains.