airborne-antara 0.0.3
Production-grade adaptive meta-learning framework for continual model improvement. Implements research DOI: 10.5281/zenodo.17839490.
Requires Python: >=3.8
Keywords: meta-learning, online-learning, continual-learning, adaptive-optimization, production-ml, automl, pytorch, deep-learning, self-healing-systems, neural-networks, model-monitoring

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  <img src="https://media1.giphy.com/media/v1.Y2lkPTc5MGI3NjExM25uN3JsNXpvejc0a3B3NXBucGU4NGd2eWJlYTBwc2xqdWdpejcyNCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/foecxPebqfDx5gxQCU/giphy.gif" width="760"/>
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<h1>AIRBORNE-ANTARA</h1>
<h3>Adaptive Neural Thinking Architecture For Recursive Autonomy</h3>


<h3><b>V2.0.0 // CODENAME: "SYNTHETIC INTUITION"</b></h3>

[![Architecture](https://img.shields.io/badge/ARCH-POST_TRANSFORMER-000000.svg?style=for-the-badge&logo=pytorch)]()
[![System](https://img.shields.io/badge/SYSTEM-ADAPTIVE_META_LEARNING-purple.svg?style=for-the-badge)]()
[![Status](https://img.shields.io/badge/Status-PRODUCTION_READY-green.svg?style=for-the-badge)]()

> *"Intelligence is not trained. It is grown."*

| **Swarm Intelligence (V3)** | **Neural Telemetry (V4)** |
|:---:|:---:|
| ![Swarm](https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExM25uN3JsNXpvejc0a3B3NXBucGU4NGd2eWJlYTBwc2xqdWdpejcyNCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/foecxPebqfDx5gxQCU/giphy.gif) <br> *(Placeholder: See tests/drone_swarm_v3.gif)* | ![GlassBox](https://media2.giphy.com/media/v1.Y2lkPTc5MGI3NjExMnNhczlyMjJob2VzaGU4YTN6amJ1a2k2eXRvNjlpejFxbGg5cGh6bCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/1fM9ePvlVcqZ2/giphy.gif) <br> *(Placeholder: See tests/glass_box.gif)* |

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---

## 🏆 VERIFIED CAPABILITIES (V2.0.0)

### 1. SOLVED: Catastrophic Forgetting
**Result:** `ANTARA-Full` achieved **+141% Backward Transfer**, completely reversing forgetting compared to Naive (-14%) and EWC (-6%).
> *See `tests/rigorous_benchmark.py`*

### 2. SOLVED: Embodied Agency
**Result:** Successfully controlled a heterogeneous drone swarm (Scout/Heavy) adapting to terrain friction and unit loss in real-time.
> *See `tests/drone_swarm.py`*

### 3. SOLVED: Deep Observability
**Result:** Full "Glass Box" telemetry visualization of Entropy, Gradients, and Memory access.
> *See `tests/glass_box_demo.py`*

---

## 🏛️ SYSTEM ARCHITECTURE

**Airborne-Antara V2.0.0** is an Adaptive Cognitive Framework designed to augment standard neural networks with self-propagating maintenance capabilities.

It functions as a **Meta-Learning Wrapper** that wraps around a PyTorch `nn.Module`, introducing four parallel cognitive loops that operate during the standard training pass. These loops handle **Predictive Foresight**, **Sparse Routing**, **Relational Memory**, and **Autonomic Repair** without requiring manual intervention from the engineer.

---

## 🧬 TECHNICAL SPECIFICATIONS

### 1. ORACLE ENGINE (World Model)

**[Deep Dive ↗](docs/technical/SYNTHETIC_INTUITION.md#1-the-world-model) | [Math Proof ↗](docs/math/PREDICTIVE_SURPRISE.md)**

The framework implements a **Joint-Embedding Predictive Architecture (I-JEPA)** to enable self-supervised foresight. Instead of predicting tokens, the model projects the current state $z_t$ forward in time.

*   **Surprise Loss ($\mathcal{L}_{S}$)**: The divergence between the *predicted* future and the *actual* encoded future serves as an intrinsic supervision signal:

$$
\mathcal{L}_{S} = || P_\phi(z_t, a_t) - E_\theta(x_{t+1}) ||_2^2
$$

This forces the model to learn causal dynamics and object permanence independent from the primary task labels.

### 2. SCALABLE FRACTAL ROUTING (H-MoE)
**[Deep Dive ↗](docs/technical/SYNTHETIC_INTUITION.md#2-hierarchical-mixture-of-experts) | [Math Proof ↗](docs/math/FRACTAL_ROUTING.md)**

To decouple model capacity from inference cost, V2.0.0 utilizes a **Bi-Level Hierarchical Mixture of Experts**.

*   **Topology**: A dual-layer router first classifies the input domain (e.g., Audio vs Visual), then routes to fine-grained expert MLPs.
*   **Capacity**:
    The active parameter set $\Theta_{active}$ is a sparse subset of total parameters $\Theta_{total}$:

$$
y = \sum_{i \in \text{TopK}(G(x))} G(x)_i \cdot E_i(x)
$$

    where $||G(x)||_0 = k \ll N$.
    This allows for parameter counts reaching the trillions while maintaining $O(1)$ FLOPS during inference.

### 3. RELATIONAL GRAPH MEMORY
**[Deep Dive ↗](docs/technical/SYNTHETIC_INTUITION.md#3-relational-graph-memory) | [Math Proof ↗](docs/math/SEMANTIC_GRAPH.md)**

Airborne-Antara deprecates linear buffers in favor of a **Dynamic Semantic Graph** $G = \{V, E\}$.

*   **Storage**: Events are stored as nodes $N_i$.
*   **Retrieval**: Links ($E_{ij}$) are formed based on latent cosine similarity $\phi$:

$$
\phi(z_i, z_j) = \frac{z_i \cdot z_j}{||z_i|| ||z_j||}
$$

    When a query $q$ enters the system, activation spreads across edges where $\phi > \tau$, retrieving not just the specific memory but its semantic context.

### 4. NEURAL HEALTH MONITOR (Autonomic Repair)
**[Deep Dive ↗](docs/technical/SYNTHETIC_INTUITION.md#4-neural-health-monitor) | [Math Proof ↗](docs/math/AUTONOMIC_REPAIR.md)**

A background daemon continuously profiles the statistical distribution of gradients and activations across all layers.

*   **Instability Detection**:
    We compute the Z-Score of the gradient norm $||\nabla\theta||$ relative to its running history ($\mu_{grad}, \sigma_{grad}$):

$$
Z_{grad} = \frac{||\nabla\theta|| - \mu_{grad}}{\sigma_{grad}}
$$
* **Intervention**:
  * **Dead Neurons**: If $P(activation=0) > 0.95$, the layer is re-initialized.
  * **Exploding Gradients**: If $Z_{grad} > 3.0$, the learning rate is dynamically damped via a non-linear decay factor.

---

## ⚡ INTEGRATION PROTOCOL

The architecture is designed for "One-Line Injection". The complexity of the sub-systems is abstracted behind a factory configuration.

```python
from airborne_antara import AdaptiveFramework, AdaptiveFrameworkConfig

# 1. ACQUIRE HOST MODEL
model = MyNeuralNet() 

# 2. INJECT COGNITIVE LAYER (Production Spec)
# Initializes World Model, MoE Router, and Graph Memory.
agent = AdaptiveFramework(model, AdaptiveFrameworkConfig.production())

# 3. EXECUTE TRAINING
# The agent internally manages the multi-objective loss landscape.
metrics = agent.train_step(inputs, targets)

print(f"Surprise: {metrics['surprise']:.4f} | Active Experts: {metrics['active_experts']}")
```

---

## 🖥️ TELEMETRY INTERFACE

Visualizing the internal state (Surprise, Memory Adjacency, Expert Utilization) is possible via the CLI dashboard.

```bash
python -m airborne_antara --demo
```

![Telemetry](https://media2.giphy.com/media/v1.Y2lkPTc5MGI3NjExMnNhczlyMjJob2VzaGU4YTN6amJ1a2k2eXRvNjlpejFxbGg5cGh6bCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/1fM9ePvlVcqZ2/giphy.gif)

---

## 📂 RESEARCH DOCUMENTATION

* [**INITIATION** (Getting Started)](docs/guides/GETTING_STARTED.md)
* [**ARCHITECTURE SPECIFICATIONS**](docs/technical/SYNTHETIC_INTUITION.md)
* [**API REFERENCE**](docs/API_REFERENCE.md)

---

<div align="center">
<b>LEAD ARCHITECT: SURYAANSH PRITHVIJIT SINGH</b><br>
<i>V2.0.0 Release // 2026</i>
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