We introduce NarrativeMind, a previously-unexplored cooperative neural-symbolic decoder in Arabic NLP that dynamically injects dialect-specific cultural constraints during generation. Our approach achieving +3.5 BLEU improvement over AraBERT (from 26.3 to 29.8) while reducing MSA bias by 58%, addressing the critical gap where traditional Arabic narrative systems inadequately capture the rhetorical sophistication embedded in morphologically complex literary forms. The hybrid architecture seamlessly integrates classical Arabic structures into BLOOMZ’s decoding through weighted interpolation, preserving essential rhetorical devices. For instance, it maintains patterns in (wisdom in words, blessing in currency) and (jinās) wordplay as demonstrated in (the speaker said to the killer). Unlike rigid frameworks, NarrativeMind adapts fluidly across Modern Standard Arabic and six regional dialects, particularly benefiting under-resourced Maghrebi varieties. Our real-time multi-dialect collaboration employs adaptive constraint weighting, optimizing both BLEU coherence and our novel CulturalScore metric. This metric derives from 2,500 expert-annotated templates spanning classical (maqāmāt) to contemporary , ensuring comprehensive cultural representation. MADAR corpus evaluation (n=12,000) demonstrates substantial improvements: BLEU scores reached \(29.8 \pm 0.4\) versus \(27.1 \pm 0.3\) for baselines, with dialectal accuracy achieving \(\kappa = 0.76\) compared to 0.70. Human evaluation involving 15 linguists and 185 native speakers validates 82.5% cultural authenticity ( \(p < 0.01\) ), confirming effective cross-regional story co-creation while preserving dialectal integrity.