# AIRBORNE.HRS

V2.0.0 // CODENAME: "SYNTHETIC INTUITION"

"Intelligence is not trained. It is grown."

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🏛️ SYSTEM ARCHITECTURE

AirborneHRS V2.0.0 is an Adaptive Cognitive Framework designed to augment standard neural networks with self-propagating maintenance capabilities.

It functions as a Symbiotic Layer 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.

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🧬 TECHNICAL SPECIFICATIONS

1. ORACLE ENGINE (World Model)

Deep Dive ↗ | Math Proof ↗

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 ↗ | Math Proof ↗

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 ↗ | Math Proof ↗

AirborneHRS 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 ↗ | Math Proof ↗

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.

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⚡ INTEGRATION PROTOCOL

The architecture is designed for "One-Line Injection". The complexity of the sub-systems is abstracted behind a factory configuration.

from airbornehrs 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']}")

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🖥️ TELEMETRY INTERFACE

Visualizing the internal state (Surprise, Memory Adjacency, Expert Utilization) is possible via the CLI dashboard.

python -m airbornehrs --demo

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📂 RESEARCH DOCUMENTATION

• INITIATION (Getting Started)
• ARCHITECTURE SPECIFICATIONS
• API REFERENCE

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LEAD ARCHITECT: SURYAANSH PRITHVIJIT SINGH
V2.0.0 Release // 2026