airborne-antara 0.0.8

Production-grade adaptive meta-learning framework for continual model improvement. Implements research DOI: 10.5281/zenodo.17839490.

AIRBORNE-ANTARA

Adaptive Neural Thinking Architecture For Recursive Autonomy

V8.0 // CODENAME: "SENTIENT" EDITION

"Intelligence is no longer just trained. It is synthesized through awareness."

Autonomous Consciousness	Unified Memory
Recursive Workspace V2	Holographic Saliency Pooling

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🏆 SENTIENT CAPABILITIES (V8.0)

[!IMPORTANT] ANTARA V8.0 is a non-destructive cognitive wrapper. It does not replace your model weights; it builds a "conscious" manifold around them.

1. Unified Memory (SI + EWC + OGD)

Result: Eliminated catastrophic forgetting in multi-task scenarios by orthogonalizing new learning directions against historical importance manifolds.

See airborne_antara/memory.py

2. Recursive Consciousness (System 2)

Result: Enabled slow, deliberative reasoning over complex tasks using the Recursive Global Workspace. The model now generates and evaluates thought traces before final execution.

See airborne_antara/consciousness_v2.py

3. Perception Gateway (Multi-Modal)

Result: Native support for Vision, Audio, and Text via ViT-style encoders with Dynamic Positional Interpolation for variable input scales.

See airborne_antara/perception.py

4. Autonomic Health (Neural Shivering)

Result: Self-healing neural substrate. The monitor detects and resets stale activations (ReLU death) and dampens gradient explosions automatically.

See airborne_antara/core.py

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🧬 THE 4 PILLARS OF SENTIENCE

1. CONSCIOUSNESS V2 (Global Workspace)

Technical Deep Dive ↗

Implements System 2 Thinking. Instead of a single forward pass, the model projects states into a recursive workspace to simulate "thinking about the problem."

• Thought Trace: Internal hidden state evolution logged as "telemetry" for debugging.
• Recursive Workspace: Dynamic number of internal reasoning loops based on task entropy.

2. HOLOGRAPHIC MEMORY (Unified Handler)

Technical Deep Dive ↗

Combines Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), and Orthogonal Gradient Descent (OGD).

• Saliency Pooling: Dynamically prioritizes historical parameters to prevent erasure.
• Experience Replay: Generative replay of "dreams" during idle cycles to consolidate learning.

3. MULTI-MODAL PERCEPTION GATEWAY

Technical Deep Dive ↗

Unified manifold for Vision (Transformers), Audio (Spectral-Temporal), and Text.

• Positional Interpolation: Scalable attention windows for high-resolution vision.
• Modality Fusion: Cross-modal attention tokens for joint reasoning.

4. AUTONOMIC HEALTH MONITOR

Technical Deep Dive ↗

A background daemon tracking the "Neural Health" of the host model.

• Neural Shivering: Injecting controlled stochastic noise to prevent saturation.
• Gradient Centralization: Modern optimization to stabilize deep manifold learning.

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🧪 RESEARCH / EXPERIMENTAL (V9.2)

[!CAUTION] These features are in preview for the NeurIPS ablation suite and may exhibit instability in production.

• Self-Awareness V2: Metacognitive engine calculating "Confidence" and "Competence" in real-time.
• I-JEPA World Model: Predictive foresight for world-dynamic modeling.
• Holographic Compression: Next-gen memory storage with $O(log N)$ retrieval complexity.

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⚡ INTEGRATION PROTOCOL

The architecture is designed for "One-Line Cognitive Injection".

import torch
from airborne_antara import AdaptiveFramework, PRESETS

# 1. DEFINE YOUR PYTORCH MODEL (Transformer, CNN, etc.)
model = MySubstrate() 

# 2. INJECT SENTIENT LAYER
# Uses the 'production' preset: Consciousness V2 + Unified Memory + MoE
agent = AdaptiveFramework(model, PRESETS.production())

# 3. CONSCIOUS TRAINING LOOP
# The agent handles Mixed Precision (AMP), Memory Consolidation, and Thought Tracing
for inputs, targets in dataloader:
    metrics = agent.train_step(inputs, target_data=targets)
    
    print(f"Loss: {metrics['loss']:.4f} | Surprise: {metrics['surprise']:.4f}")
    print(f"Cognitive Mode: {metrics['mode']}") # [NORMAL, NOVELTY, PANIC]

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🖥️ TELEMETRY INTERFACE

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

python -m airborne_antara --demo

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📂 RESEARCH DOCUMENTATION

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

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LEAD ARCHITECT: SURYAANSH PRITHVIJIT SINGH
V8.0 "Sentient" Release // 2026