Edge Knowledge in Cognitive Art: Munch Digital Twin

In an era where artificial intelligence is rapidly expanding into creative domains, the challenge of modeling human-like cognition and emotion in generative processes becomes increasingly central. The present study was made in connection with the exhibition of Munch’s works held in Rome from February to June 2025. Indeed, the paper introduces the concept of a Cognitive Digital Twin grounded in the Super Time-Cognitive Neural Network (STCNN) framework and applies it to the case of Edvard Munch, the iconic Norwegian expressionist. The proposed system—Munch Digital Twin—goes beyond static generative models by integrating temporal, emotional, and cognitive dimensions through a complex-valued time representation t = a + i·b, where a denotes chronological time and b encodes imagination, memory, and creativity. We define Edge Knowledge as an output-stage re-ranking criterion that admits a generated response only where corpus evidence, knowledge-graph constraints and the LLM surface jointly agree (the boundary, or ‘edge’, between documented identity and machine inference). STCNN allows this twin to process real inputs (text, visual prompts, emotional cues) and generate outputs that reflect both the rational and expressive styles of Munch. The imaginary components of the network enable speculative and affective expansions of known artworks—such as reinterpreting The Scream under new emotional or social contexts. This paper presents the theoretical underpinnings of cognitive digital twins, the architecture of the STCNN-based model, and a prototype implementation trained on Munch’s paintings, letters, and critical essays. The system—comprising a GPT-4-Turbo cloud profile and a 4-bit LLaMA-2-13B edge profile for language, Stable Diffusion 1.5 + LoRA for image generation, a Neo4j knowledge graph, and FAISS retrieval—is trained on approximately 600 letters, 100 artworks, and Munch’s diaries and criticism, and evaluated across 100 interactive sessions with 14 students and expert raters. Headline results against an unconditioned baseline include CLIPScore +13.8%, FID −25.5% (small-sample, indicative), and emotion-cosine similarity +44.9%. Ethical implications surrounding posthumous digital emulation, authorship, and emotional manipulation are also discussed. The Munch Digital Twin represents a new paradigm in AI-driven art, where machines do not merely replicate, but collaborate across time with human legacies, enabling an anticipatory and emotionally intelligent form of computational creativity. This work is primarily a conceptual and architectural contribution, supported by a proof-of-concept prototype and a preliminary, non-controlled user study; the quantitative results are indicative and not yet confirmatory.