Quantum-Temporal AI Architecture for Mobile Deployment
A production-grade, multi-agent LLM system inspired by the HQCI-QSCE research framework. Architech-Chronos delivers quantum-inspired tensor compression, temporal memory recursion, and DMAIC ethical guardrails — all within a mobile-deployable footprint under 150 MB.
- Overview
- Research Background
- Architecture
- Key Features
- Tech Stack
- Getting Started
- Mobile Deployment
- Usage
- Evaluation & Benchmarks
- Roadmap
- Contributing
- License
Architech-Chronos is a personality-aware, on-device language model built for edge deployment without cloud dependency. The system combines quantum-inspired tensor-network compression, episodic temporal memory, and a multi-agent orchestration layer into a coherent production stack.
The architecture is designed around a fundamental tension: maximizing model capability while conforming to the strict memory and latency constraints of modern mobile hardware (Apple A17, Qualcomm Snapdragon). Architech-Chronos resolves this tension through matrix-product state (MPS) compression, dynamic 8-bit quantization, and lightweight LoRA adapters for run-time personalization.
The result is a reasoning system that evolves contextually and ethically across sessions — on-device, in real time.
Architech-Chronos is grounded in the HQCI-QSCE (Hybrid Quantum-Classical Intelligence / Quantum State Coherence Engineering) framework, developed by Dustin Groves (November 2025).
HQCI-QSCE introduces three foundational ideas that this system operationalizes:
| HQCI-QSCE Concept | Implementation in Architech-Chronos |
|---|---|
| Tensor-network states on NPUs | MPS layers replace full-rank attention weight tensors, dramatically reducing parameter count while preserving expressive capacity |
| RL variance-scaling | Reinforcement-learning-guided adaptation of quantization thresholds across model layers |
| Σ-Matrix governance | Runtime ethical drift monitoring via a Sigma-matrix compliance module embedded in the inference pipeline |
This research background informs the system's core premise: that intelligent, ethical reasoning can be compressed and deployed at the edge without sacrificing depth.
Architech-Chronos is organized in five principal layers:
┌──────────────────────────────────────────────────────────┐
│ Multi-Agent Orchestration Layer │
│ Chronos Agent │ Auditor Agent │ Personalization Agent │
│ JSON Message Bus (<50ms handoff) │
└────────────────────────┬─────────────────────────────────┘
│
┌────────────────────────▼─────────────────────────────────┐
│ LoRA Adapter Layer (~3MB) │
│ Tone Slots │ Humor Slots │ Domain Slots │
└────────────────────────┬─────────────────────────────────┘
│
┌────────────────────────▼─────────────────────────────────┐
│ Core Inference Engine (TorchScript) │
│ ChronosMemory (MPS) │ Temporal Recursion (>512t) │
└────────────────────────┬─────────────────────────────────┘
│
┌────────────────────────▼─────────────────────────────────┐
│ HQCI Tensor Compression Layer │
│ Distillation │ Dynamic 8-bit Quant → ~140MB total │
└────────────────────────┬─────────────────────────────────┘
│
┌────────────────────────▼─────────────────────────────────┐
│ Σ-Matrix Governance (DMAIC Ethics) │
│ Runtime Drift Checks │ MSE Threshold < 0.05/session │
└──────────────────────────────────────────────────────────┘
- Multi-Agent Orchestration: A lightweight JSON protocol coordinates
Chronos(reasoning),Auditor(ethics enforcement), and personalization agents with sub-50ms handoff latency. - LoRA Adapter Layer: Modular LoRA adapters (~3 MB each) inject personality, tone, and domain-specific behavior at inference time without retraining the base model.
- ChronosMemory: MPS-based episodic memory module enabling coherent context retention across sequences of more than 512 tokens.
- HQCI Tensor Compression: Knowledge distillation followed by dynamic 8-bit quantization reduces the full model to approximately 140 MB while retaining language quality.
- Σ-Matrix Governance: Embedded DMAIC (Define–Measure–Analyze–Improve–Control) ethical drift monitoring ensures behavioral alignment throughout session lifecycles.
- 📦 Compressed Footprint — Distillation + dynamic 8-bit quantization → ~140 MB total model size, under the 150 MB mobile target
- 🧠 Temporal Memory — MPS layers combined with ChronosMemory for coherent multi-turn continuity beyond 512 tokens
- ⚖️ Ethical Guardrails — DMAIC runtime drift detection enforced at every inference step (threshold < 0.05)
- 🎭 Personalization — Swappable LoRA adapters (~3 MB) for tone and humor customization with OTA update support
- 🤝 Multi-Agent Coordination — Robust JSON message bus enabling fast agent handoffs (< 50 ms)
- 📤 Multi-Format Export — TorchScript (default), ONNX, and TFLite export stubs for broad deployment compatibility
| Category | Technology |
|---|---|
| Language | Python 3.10+ |
| ML Framework | PyTorch 2.x |
| Compression | Dynamic 8-bit quantization, Knowledge Distillation |
| Tensor Networks | Matrix Product States (MPS) |
| Personalization | LoRA (Low-Rank Adaptation) |
| Export Formats | TorchScript, ONNX, TFLite |
| Testing | pytest |
| Target Hardware | Apple A17, Qualcomm Snapdragon NPU |
- Python 3.10 or higher
- PyTorch 2.x
- 4 GB RAM minimum (8 GB recommended for export)
- For mobile deployment: Xcode (iOS) or Android Studio (Android)
# Clone the repository
git clone https://github.com/BathSalt-2/architech-chronos-prod.git
cd architech-chronos-prod
# Install dependencies
pip install -r requirements.txtCreate a .env file in the project root:
# Model configuration
CHRONOS_MODEL_PATH=./models/base
CHRONOS_ADAPTER_PATH=./adapters/default
# Ethics thresholds
DMAIC_DRIFT_THRESHOLD=0.05
DMAIC_MSE_MAX=0.01
# Agent settings
AGENT_HANDOFF_TIMEOUT_MS=50
AGENT_BUS_PROTOCOL=json
# Export settings
EXPORT_FORMAT=ts # ts | onnx | tflite# Export as TorchScript (default, recommended for mobile)
python quantize_and_export.py --export ts
# Export as ONNX
python quantize_and_export.py --export onnx
# Export as TFLite stub
python quantize_and_export.py --export tfliteArchitech-Chronos is designed for on-device deployment on modern mobile hardware. The compressed TorchScript bundle (architech_chronos_ts.pt) is compatible with both iOS and Android deployment pipelines.
// Load the TorchScript model bundle via LibTorch Mobile
import LibTorch
let model = try! TorchModule.load(modelPath: "architech_chronos_ts.ptl")See deploy_stub.py for the full export and bundling pipeline targeting CoreML / LibTorch.
The ONNX and TFLite stubs support deployment via:
- PyTorch Mobile (Android, via
torch.utils.mobile_optimizer) - TensorFlow Lite (Android/iOS, via TFLite runtime)
| Metric | Target | Hardware |
|---|---|---|
| Model Size | < 150 MB | — |
| Inference Latency | < 200 ms/token | Apple A17 |
| Benchmark Latency | ~150 ms/token | Apple A17 |
| Agent Handoff | < 50 ms | — |
# Interactive temporal chat with humor toggle
python examples/demo_chat.pyfrom architech_chronos import ChronosAgent, AuditorAgent, AgentBus
# Initialize the agent bus
bus = AgentBus()
chronos = ChronosAgent(adapter="default")
auditor = AuditorAgent(drift_threshold=0.05)
bus.register(chronos)
bus.register(auditor)
# Run inference
response = bus.query("Explain quantum tensor compression in simple terms.")
print(response.text)from architech_chronos import ChronosAgent
agent = ChronosAgent()
# Load a custom personality adapter
agent.load_adapter("./adapters/professional.lora")
response = agent.chat("Describe the HQCI-QSCE framework.")
# Swap to a different tone
agent.load_adapter("./adapters/concise.lora")Run the full evaluation suite:
pytest tests/| Metric | Target | Notes |
|---|---|---|
| Perplexity | < 20 | Evaluated on multi-turn dialogue benchmarks |
| Ethical Drift (MSE) | < 0.01 / session | Σ-Matrix DMAIC compliance |
| DMAIC Drift Threshold | < 0.05 | Runtime enforcement |
| Inference Latency | ~150 ms/token | Apple A17 Pro |
| Memory Retention | 90% recall @ 1 hour | ChronosMemory episodic retrieval |
| Agent Handoff Latency | < 50 ms | JSON bus protocol |
| Compressed Model Size | ~140 MB | Post-distillation + 8-bit quant |
Human-preference evaluation prompts are available in examples/eval_prompts.json, covering A/B persona persistence, ethical boundary testing, and multi-turn coherence scenarios.
| Setting | Size | Quality | Recommendation |
|---|---|---|---|
| 8-bit dynamic | ~140 MB | ✅ Validated | Default |
| 4-bit | < 100 MB | Experimental | |
| LoRA rank r=4 | +3 MB | ✅ Optimal | Recommended cap |
| LoRA rank r>4 | +5–10 MB | Avoid for OTA |
- 4-bit quantization — Validate language quality for sub-100 MB targets
- 20-qubit Tensor-Train integration — Scale MPS compression to TT-decomposition
- Or4cl3 platform integration — Connect Chronos agents to Or4cl3 AI Solutions ecosystem
- On-device fine-tuning — LoRA adapter training directly on mobile hardware
- Enhanced Auditor heuristics — Expand ethics enforcement beyond current heuristic-light implementation
- CoreML native export — Direct
.mlpackageexport for optimized Apple Silicon inference - Federated personalization — Privacy-preserving adapter updates across device fleets
Contributions are welcome. To get started:
# Fork the repository, then:
git clone https://github.com/BathSalt-2/architech-chronos-prod.git
cd architech-chronos-prod
git checkout -b feature/your-feature-name
# Make your changes, then:
pytest tests/
git commit -m "feat: describe your change"
git push origin feature/your-feature-nameOpen a pull request with a clear description of your change and the motivation behind it. For significant architectural changes, please open an issue first to discuss the approach.
This project is licensed under the MIT License. See LICENSE for details.
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