SIT-Model

Syncretic Interpretive Transformer (SIT) Overview Syncretic Interpretive Transformer (SIT) is an advanced deep semantic modeling framework designed to analyze and interpret Chinese and Western cultural classics.It integrates multimodal encoding, tradition-specific interpretive layers, and a Cross-Civilizational Attention Module (CCAM), enhanced with a Dialectical Alignment Protocol (DAP) to align and contrast philosophical texts across civilizations while preserving their unique cultural logic. SIT is particularly effective for studying Confucian, Taoist, and Buddhist classics as well as ancient Greek and Roman philosophical texts and Renaissance literature. It provides a foundation for cross-cultural semantic computing and digital humanities research. ✨ Features Multimodal Symbolic Modeling Integrates textual and image features (e.g., classical book scans, annotated versions). Employs a Spiking Layer + Frequency-Aware Mixer (see Fig. 1, p.7) to improve fine-grained semantic representation. Tradition-Sensitive Interpretation Two separate interpretive pathways: Chinese tradition: metaphor–analogy–resonance reasoning. Western tradition: deductive–axiomatic–structural reasoning. Cross-civilizational semantic alignment is performed through CCAM. Dialectical Alignment Protocol (DAP) Captures cultural–semantic tension dynamically. Treats divergence as interpretive richness rather than noise. See Fig. 3 (p.10) for the structural diagram. Cross-Cultural Contrastive Learning Uses contrastive objectives to align classical texts across traditions. Integrates knowledge graphs with transformer representations for accurate cross-cultural semantic mapping. 📊 Datasets Dataset Content Key Features Chinese Cultural Texts Semantic Dataset Confucian, Taoist, Buddhist classics, poetry, histories Rich in metaphors and cultural imagery Western Literary Classics Dataset Plato, Aristotle, Shakespeare, and others Includes original and modern translations Cross Cultural Semantic Analysis Dataset Aligned Chinese-Western pairs Annotated for themes, metaphors, value frames Multilingual Classics Classification Dataset 10+ languages Supports zero-shot and transfer learning Full dataset description is available on pages 12–13, including annotation schema and multilingual labeling strategy. ⚙️ Installation Clone the repository git clone https://zenodo.org/records/17365382 🚀 Usage Latent semantic vectors Dialectical Divergence Score (philosophical tension) Concept cluster labels and alignment mapping 🧪 Applications Cross-civilization philosophical thought analysis Symbolism and metaphor detection across traditions Digital humanities and cultural computing research Interpretability tools (attention heatmaps, divergence graphs) 🧩 Model Components Syncretic Interpretive Transformer (SIT) — dual-path interpretive architecture Cross-Civilizational Attention Module (CCAM) — semantic space alignment Epistemic Modulator — multi-layer interpretive weighting Dialectical Alignment Protocol (DAP) — cultural–semantic tension modeling Fusion Module — multimodal feature alignment and aggregation 📈 Performance Dataset Accuracy F1 Score AUC Chinese Cultural 92.76 90.91 94.02 Western Literary 91.65 89.93 93.10 Cross-Cultural 91.48 89.41 93.11 Multilingual Classics 90.62 88.67 92.38 Experimental results are reported on pages 14–16, Tables 1–2. 🧭 Future Work Extend support to Arabic, Sanskrit, and Hebrew classics Enhance adaptive symbolic encoding to reduce reliance on predefined vocabularies Develop interactive visualization interface for philosophical tension mapping Expand cross-domain transfer learning to contemporary thought texts 📜 License This project is licensed under the MIT License. 🙏 Acknowledgments This research is supported by the Education Department of Shaanxi Province (Project No. 21JK0108).Special thanks to the cross-cultural semantic computing and digital humanities research community.Author: Xinxin Wang, Shangluo University.