Author: Lachlan Chen, AgInTiFlow Affiliation: AgInTi Lab, LazyingArt LLC
The Imagized Language Model (ILM) treats language as an image-like object. Tokens are rendered as glyph images, mapped into factorized multi-channel codes, aligned through contrastive and structural objectives, and generated or infilled through diffusion-style denoising over code grids.
This idea is useful because it bypasses some assumptions of conventional tokenization. It can work across scripts by rendering text visually, it can learn from glyph images and question-answer corpora, and it can represent a token as a product of multiple discrete channels rather than a single opaque embedding.
- Encode words, characters, or unknown scripts as rendered glyph images.
- Learn shared product-codebook addresses that are invertible, memory-like, and cross-lingual.
- Align glyph features, token codes, and QA semantics with InfoNCE and Gram-structure losses.
- Use image-style diffusion or inpainting to reconstruct masked language grids.
- Position ILM as a bridge between language modeling, visual representation learning, historical scripts, and multilingual low-resource NLP.
- Source repository: github.com/lachlanchen/ImagizedLanguageModel
- PDF: Imagized Language Model
- PDF: Structured Technical Documentation
- PDF: Deep Dive
- PDF: Math Validation
- PDF: Cross-Lingual Derivation