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  <img src="./banner.svg" width="100%" />
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<h1 align="center">🧠 CRS-LM</h1>

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  Context Reconstruction System for Language Models
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## 🚀 What is CRS-LM?

CRS-LM is a **context optimization layer**.

Instead of:
> Feeding full context

We:
> Filter → Compress → Reconstruct → Predict

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## ⚙️ Pipeline

```text
Input → Tokenizer → CRS → TinyLM → Output
🧠 CRS Engine
✂️ Noise removal
📉 Token reduction
🔄 Context reconstruction
🎯 Signal preservation
📊 Metrics
Metric	Value
Token Reduction	~41%
Speed	Slightly faster
Loss	Increased
⚠️ Status
- Not production ready
+ Research prototype
+ High potential
📁 Modules
crs-lm/
├── model/
├── tokenizer/
├── crs/
├── train.py
├── infer.py
├── eval.py
🧪 Example
text = "AI needs better context"

tokens = tokenize(text)
filtered = crs_filter(tokens)
output = model.predict(filtered)
🧬 Roadmap
Smarter CRS scoring
Graph-based context
Semantic recovery
🧠 Philosophy

Context > Tokens