S heaf-Aware Multilingual Corpus Reducer . 🔭 Design overview Goal – Collapse every language’s lexicon into the smallest possible global manifold of “meaning atoms.” Each language is treated as a local section of the sheaf; alignment across languages produces a global section that unifies semantic content. 🧩 Architecture modules Module Function Output sheaf_core.py defines the category objects: Sheaf , Section , Morphism , Chart algebraic backbone embedding_loader.py loads v6-core embeddings (4096-D) into normalized tensors tensor map morphology_map.py rules for morpheme decomposition & phoneme merging per-language charts alignment_engine.py finds cross-lingual equivalences via cosine / mutual-info gluing morphisms reduction_engine.py cohomology reduction: remove redundant sections, preserve non-exact classes reduced semantic basis globalizer.py constructs global section (universal embedding manifold) unified dictionary visualizer....
Posts
- Get link
- X
- Other Apps
Concept: Pantographic AI — a universal translator that scales & maps patterns across models, languages, and modalities Understood. Below is a formal, focused design for a Pantographic AI system that traces, scales, and preserves pattern relationships while translating between modalities (text ⇄ image ⇄ audio ⇄ code ⇄ simulations, etc.). I provide architecture, training strategy, API, evaluation plan, and a minimal prototype blueprint (PyTorch-style pseudocode) you can run or iterate from immediately. 1 — Core idea (one-sentence) Create a shared, scale-aware latent hub and a set of modality adapters + pantographic mappers so any input modality can be encoded into a common geometric latent where controlled scale (zoom), abstraction, and stylistic transforms map consistently to any output modality. 2 — High-level architecture Modality Encoders (E_m) Per-modality encoder that maps raw input to latent tokens. Examples: transforme...