AI and the Grammar of Visual Narrative: A Neuro-Symbolic Approach to Cinematic Editing Fundamentals
Contemporary artificial intelligence systems demonstrate remarkable capabilities in describing individual images yet remain fundamentally deficient in understanding visual sequences—a limitation that undermines their application in analyzing and creating audiovisive artworks. This deficiency occurs within what philosopher Byung-Chul Han diagnoses as the "crisis of narration": an era where coherent meaning structures are eroded by fragmented digital information flows. This paper proposes a neuro-symbolic methodology to address both the technical and cultural dimensions of this challenge. Inspired by strategic frameworks from OpenAI experts, we formalize the "Ten Principles of Smooth Editing"—derived from classical film practice—into algorithmic tasks, enabling AI to evaluate visual coherence and narrative logic. The core contribution lies in a triadic architecture integrating neural perception, symbolic reasoning, and culturally-adaptive knowledge graphs. Through comparative analysis of existing approaches (pure neural networks vs. hybrid systems) and design of a Chinese cultural aesthetics subgraph, this study articulates principles for developing AI's "cinematic literacy." This work does not aim to replace human creativity but to establish foundations for AI systems that understand—and respect—the grammar of visual storytelling across cultural contexts. By centering formal rules and cultural specificity, the framework offers a pathway to counter algorithmic fragmentation and rebuild meaningful continuity in audiovisual media.
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