Mamba-2 SSM. Mixture of Experts. Mixture of Models. Intuition Engine. Quantum optimisation. The sovereign cognition engine that processes the battlefield.
49GB of data compressed into a 16-dimensional state vector
Vision, NLP, anomaly detection, spatial reasoning
Best-in-class model per category, wrapped in SIGIL
DEFONEOS cognition is built on the "Sandwich Architecture" — Mamba-2 in the middle (long-context compression), Left Brain MoE on one side (analytical reasoning), Right Brain MOM on the other (perception), with SOV3 as the sovereign binding layer.
Combines left + right results into a single sovereign answer. Ed25519 SIGIL signed. Routes queries to the correct hemisphere.
Mixture of Experts for analytical tasks.
• Math computation (sympy)
• Logic validation (fallacy detection)
• Compliance checking (30 frameworks)
• Forecasting + pattern detection
• Code explanation + generation
• Dose-response curve analysis
• Multi-stakeholder council reasoning
Mixture of Models for sensory tasks.
• Vision (Kimi K2.5 multimodal)
• Audio (Whisper STT)
• Gesture detection (37 gestures)
• Spatial query (what's at this location)
• Physical simulation
• Video understanding
• World state observation
The heart of DEFONEOS cognition. A State-Space Model that compresses unlimited context into a fixed 16-dimensional state vector. Unlike Transformers (which scale quadratically with context), Mamba-2 scales linearly. This means DEFONEOS can process days of sensor data without running out of memory.
Input: Streaming SIGILs (1Hz — one per second)
State: 16 floats (positions [0-15])
Output: Wisdom string + energy + complexity + tick count
Every input and output is wrapped in a SIGIL — an Ed25519-signed hash-chained receipt. The entire cognition chain is auditable. Regulators can replay any decision and verify its integrity.
DEFONEOS selects the best model for each task. No one model is best at everything — the router picks the right tool for the job.
| Model | Task | Why It's Best | Speed |
|---|---|---|---|
| YOLOv8 | Object detection (drones, vessels, vehicles) | Real-time detection. SOTA on COCO. 250 FPS on Orin AGX. | 4ms |
| Whisper | Speech-to-text (radio comms, voice commands) | OpenAI's model. 99 languages. Noise-robust. | 18ms/s |
| Llama-3.1-70B | General reasoning | Open weights. 70B parameters. Apache 2.0. | 180 tok/s |
| DeepSeek-R1 | Deep reasoning / chain-of-thought | RL-trained reasoning. SOTA on AIME, MATH. | 80 tok/s |
| Falcon3 | Code generation + repair | Fast code model. 10B parameters. | 200 tok/s |
| Qwen2.5:3B | Fast routing / triage | 3B params. 12ms inference. Edge-deployable. | 12ms |
| Moondream | Vision-language (image Q&A) | 2B params. Multimodal. Edge-deployable. | 25ms |
| Nomic-Embed | Vector embeddings (semantic search) | 8K context. Open weights. SOTA retrieval. | 3ms/query |
| Mamba-2 | Long-context compression (SSM) | 16-dim state. Linear scaling. 1Hz ingest. | 1ms/update |
| Mava (RL) | Multi-agent reinforcement learning | Swarm coordination. MARL SOTA. | GPU-dependent |
| OpenAthena | Geospatial computation (photogrammetry) | Pixel-to-GPS. Drone to coordinates. | 500ms |
| Batear | Acoustic detection (gunfire, drones) | $10 hardware. Open source. 95% accuracy. | Real-time |
| Nemotron-70B | Care-centred dialogue + analysis | NVIDIA's model. Emotional intelligence. | 120 tok/s |
| Kimi K2.5 | Multimodal analysis (vision + text) | 128K context. Multimodal SOTA. | 90 tok/s |
Model Router: The SOV3 router analyses each query and selects the optimal model. It considers: query type (code, reasoning, vision, fast), context length, latency requirements, and edge constraints. The router is trained on the federated_rag_log.jsonl — it learns from real usage.
The Intuition Engine is DEFONEOS's secret weapon. It detects emerging threats before threshold-based alerting systems fire. It works by compressing SIGIL streams into Mamba-2 state vectors and detecting patterns via cosine similarity.
| Metric | Value |
|---|---|
| Capture rate | 1 Hz (one SIGIL per second) |
| State dimension | 16 floats |
| Pattern detection | Cosine similarity (threshold: 0.85) |
| Min states for confirmation | 3 matching historical states |
| Average lead time | 40 seconds before threshold alert |
| History retention | Unlimited (SQLite database) |
| Daily report | Auto-generated at 04:00 UTC |
Example: Maritime dark vessel scenario. AIS data shows normal shipping for 3 hours. At T+3:02:15, a vessel's AIS goes dark. The threshold system alerts at T+3:02:15. But the Intuition Engine noticed a subtle pattern shift at T+3:01:35 — the vessel's speed and heading had drifted 0.3 degrees from its predicted course. The engine fired an intuition alert 40 seconds before the threshold system. Those 40 seconds are the difference between interdiction and loss.
DEFONEOS uses quantum-classical hybrid algorithms for three critical tasks. These run on M2 Mac quantum simulators (Qiskit) — no quantum hardware required, but ready when UK National Quantum Computer is available.
| Algorithm | Function | Defence Application |
|---|---|---|
| QAOA (Quantum Approximate Optimisation Algorithm) | Care-weight optimisation | Optimises BFT council voting weights to minimise bias and maximise accuracy |
| VQE (Variational Quantum Eigensolver) | Memory importance scoring | Scores which memories are most critical for retention. Prioritises high-value intelligence. |
| Grover's Algorithm | Quantum search | Searches 49GB data moat in O(√N) time. 10,000× faster than classical search for large datasets. |
Post-Quantum Cryptography (PQC): In addition to quantum algorithms, DEFONEOS is quantum-resistant. All signatures use NIST-standardised ML-DSA-65 (Dilithium). All key exchange uses ML-KEM-768 (Kyber). Even a future quantum computer cannot decrypt DEFONEOS communications.
BIG BRAIM wraps the 8 best-in-class open-source models — one per category — into a single sovereign brain. Every call is SIGIL-signed. The router picks the best model for each query automatically.
| Category | Champion Model | Why | Benchmark |
|---|---|---|---|
| Coding | Falcon3-10B | SOTA open-source code generation | HumanEval: 88.4% |
| Reasoning | DeepSeek-R1 | RL-trained chain-of-thought | AIME 2024: 79.8% |
| Long Context | Kimi K2.5 | 128K context window | 128K retrieval: 99.1% |
| Multilingual | Qwen2.5-72B | 29 languages | MMLU: 84.5% |
| Edge | Qwen2.5:3B | 3B params, 12ms inference | MMLU: 65.2% |
| TTS | OpenAI TTS | Natural voice synthesis | MOS: 4.5/5.0 |
| Embedding | Nomic-Embed | 8K context, open weights | MTEB: 62.3 |
| Router | SOV3 OLM | Trained on real usage | 87% routing accuracy |
For maximum rigour, DEFONEOS can run the "Sovereign-100 Train" — 1,728 mixed simulations (12 mindsets × 12 BIG BRAIM models × 12 environments). This stress-tests the system across every combination and returns the top sovereign score rankings.