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🎯 Our Mission

We research and develop linguistically-grounded optimization techniques for Large Language Models, focusing on how ancient linguistic structures can solve modern computational challenges.

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🔬 Core Research: Semantic Compression Layer

Our flagship project explores using Arabic morphological structure as an intermediate representation layer for LLMs.

The Problem

Current tokenizers fragment text inefficiently, creating a "Token Tax" that:

• Inflates compute costs quadratically
• Disadvantages 160+ high-fertility languages
• Wastes billions in training/inference costs

Our Solution

Arabic's 1,400-year-old root system offers a mathematical framework for semantic compression:

ك-ت-ب (k-t-b) = "writing"
    │
    ├─ كَتَبَ   wrote
    ├─ كِتَاب  book
    ├─ كَاتِب  writer
    ├─ مَكْتُوب written
    └─ مَكْتَبَة library

One root → Many meanings

Expected Impact:

• 🎯 30-50% token reduction
• ⚡ Up to 75% compute savings
• 🌍 Language-agnostic at the user level

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📦 Coming Soon to Hugging Face

We're working on releasing:

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🤝 Get Involved

We're an open research initiative seeking collaborators:

• 🔤 Linguists — Arabic morphology experts to validate mappings
• 🤖 ML Engineers — Tokenizer training & model fine-tuning
• 📊 Researchers — Experiment design & benchmarking
• ⚡ Systems Engineers — Inference optimization

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📚 Publications

• White Paper: Root-Based Semantic Compression (January 2026)
  • Leveraging Arabic Morphological Structure as an Optimization Layer for LLMs

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