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# Generalised MetaAttention Architecture (GMAA)

> **A Reasoning-First, Epistemically Aware Neural Architecture Beyond Transformers**

**Author & Owner:** Anurag Dongare  
**License:** Apache License 2.0  
**Project Type:** Research / Experimental  
**Domain:** NLP, Reasoning Systems, AI Safety, Post-Transformer Architectures  

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## 📌 Executive Summary

The **Generalised MetaAttention Architecture (GMAA)** introduces a new paradigm for neural language models:  
**models that reason about their own reasoning**.

Traditional Transformer and LLM architectures excel at learning statistical relationships between tokens, but they lack:

- Explicit uncertainty awareness
- Self-critique mechanisms
- Rule induction capabilities
- The ability to abstain when unsure

GMAA addresses these limitations by introducing **MetaAttention**, an attention mechanism that operates over *reasoning paths instead of tokens*, coupled with epistemic and self-evaluative modules.

This repository provides:
- A **from-scratch implementation**
- **Streaming training on Common Crawl**
- **Hybrid integration with Large Language Models**
- **Evaluation and benchmarking tools**

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## 🎯 Core Idea

> **Attention should not only relate tokens — it should evaluate beliefs.**

MetaAttention enables a model to:
- Form a **global reasoning state**
- Attend over internal representations from that state
- Judge *how confident it should be* in its outputs

This makes GMAA suitable for **trust-sensitive, safety-critical, and reasoning-heavy applications**.

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## 🚀 Key Contributions

- 🔹 **MetaAttention Mechanism** (reasoning-path attention)
- 🔹 **Epistemic Confidence Modeling** (`Know / Infer / Unsure`)
- 🔹 **Self-Critique Loop** for incoherence detection
- 🔹 **Rule Induction Head** (neural-to-symbolic bridge)
- 🔹 **Hybrid LLM + MetaReasoner System**
- 🔹 **Streaming Foundation Training on Common Crawl (C4)**

This is **not a larger Transformer** — it is a **method-aware architecture**.

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## 🧠 Motivation

Current Large Language Models:

| Limitation | Impact |
|----------|--------|
| No uncertainty modeling | Overconfident hallucinations |
| No self-critique | Unsafe outputs |
| Token-level reasoning only | Weak global reasoning |
| No abstention | Cannot say “I don’t know” |

**GMAA embeds epistemic reasoning directly into the architecture**, rather than patching it post hoc.

> *Transformers learn correlations.  
> MetaAttention learns credibility.*

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## 🏗️ Architecture Overview

### High-Level Pipeline

Input Tokens ↓ Base Model (Transformer / LLM) ↓ MetaAttention Module ↓ Meta-Representation (Global Reasoning State) ↓ ├── Rule Induction Head ├── Epistemic Confidence Head (Know / Infer / Unsure) ├── Self-Critique Loop (Coherence Score) ↓ Decision Layer ↓ Answer / Revise / Abstain

---

## 🔬 MetaAttention: Core Innovation

### Standard Self-Attention (Transformer)

Each token attends to other tokens:

\[
\text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d}}\right)V
\]

This captures **syntactic and semantic relations**, but not reasoning quality.

---

### MetaAttention (GMAA)

MetaAttention computes a **global query** from the model’s internal state:

\[
\bar{h} = \frac{1}{n}\sum_{i=1}^{n} h_i
\]

\[
Q = W_q \bar{h}, \quad K = W_k H, \quad V = W_v H
\]

\[
\text{MetaAttention}(H) = \text{softmax}\left(\frac{QK^T}{\sqrt{d}}\right)V
\]

📌 **Key Difference:**  
The model attends *from understanding*, not from individual tokens.

---

## 🧩 Epistemic Modules

### 1. Epistemic Confidence Head
Classifies outputs into:
- **Know** – grounded factual knowledge
- **Infer** – probabilistic reasoning
- **Unsure** – insufficient evidence

### 2. Rule Induction Head
Projects neural representations into **rule-like vectors**, enabling:
- Pattern abstraction
- Consistency checks
- Symbolic reasoning interfaces

### 3. Self-Critique Loop
Outputs a coherence score indicating:
- Contradictions
- Hallucination likelihood
- Reasoning instability

---

## 🤝 Hybrid LLM + GMAA System

GMAA can operate as an **epistemic guardrail** over any LLM:

User Query ↓ LLM (Generator) ↓ Candidate Answer ↓ GMAA (Reasoning & Epistemic Evaluation) ↓ Accept / Revise / Abstain

### Benefits
- ↓ Hallucination rate
- ↑ Safety and trustworthiness
- Explicit uncertainty signaling
- Regulatory-friendly behavior

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## 🔬 Training From Scratch

GMAA supports **foundation-style training** from random initialization.

### Dataset Support
- C4 (Common Crawl)
- OSCAR
- RedPajama (planned)

### Training Characteristics
- Streaming data (no full downloads)
- Language modeling objective
- Epistemic heads trained jointly
- Compatible with Google Colab

```bash
python train_common_crawl.py

---

📊 Representative Results (Observed)

Capability	Transformer	LLM	Hybrid LLM + GMAA
Fluent Generation	✅	✅	✅
Hallucination Control	❌	❌	✅
Abstention Ability	❌	❌	✅
Epistemic Transparency	❌	❌	✅
Safety-Critical QA	Weak	Medium	Strong

---

🧪 Use Cases

✔️ Scientific & medical QA ✔️ Legal reasoning ✔️ Safety-critical decision systems ✔️ Robotics & planning ✔️ Research assistants ✔️ Trustworthy chatbots

❌ Creative writing ❌ Entertainment-focused generation

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📂 Repository Structure

.
├── marm.py                # Core MetaAttention Reasoning Model
├── meta_attention.py      # MetaAttention module
├── hybrid.py              # LLM + GMAA integration
├── train_common_crawl.py  # Streaming pretraining
├── evaluate.py            # Generation & evaluation
├── visualize/             # Architecture diagrams
├── experiments/           # Benchmarks
├── notebooks/             # Colab notebooks
├── requirements.txt
├── README.md
└── LICENSE

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🛠️ Requirements

• Python ≥ 3.9
• PyTorch ≥ 2.0
• transformers
• datasets
• accelerate
• tqdm
• numpy

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📜 License

Licensed under the Apache License 2.0.

You are free to:

• Use
• Modify
• Distribute
• Commercialize

Attribution required.

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👤 Author

Anurag Dongare Independent Researcher

Research Focus:

• Epistemic AI
• Reasoning Architectures
• AI Safety
• Post-Transformer Models

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🧭 Roadmap

• Multi-layer MetaAttention
• Distributed training
• Formal epistemic guarantees
• Benchmark suite release
• Academic paper submission (NeurIPS / ICLR)

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📖 Citation (Draft)

@misc{dongare2025gmaa,
  title={Generalised MetaAttention Architecture},
  author={Dongare, Anurag},
  year={2025},
  note={Research Preprint}
}

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⭐ Final Statement

This project does not aim to sound intelligent. It aims to know when it is not.