Omega-Cube Engine v1.5

10-Component Hierarchical Memory + Model Routing System

"Like magnetic metallic spheres forming cubes — mini-universes that rotate and adjust simultaneously, connected by hierarchy and topic, forming patterns to answer complex questions."

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What is Omega-Cube?

Omega-Cube is a multi-dimensional hierarchical graph memory system that now also routes queries to the right model shards (MARP — Component #10). 10 innovations in one engine.

#	Component	Innovation
1	TensorNode	N-dimensional simultaneous hierarchies
2	HolographicEncoder	O(1) circular convolution search (3.7ms)
3	QuantumAnnealer	Dynamic topology optimization
4	DiffusionSampler	Parallel non-autoregressive retrieval
5	GrayScaleValidator	Multi-bit truth (6 dimensions, H-Bit)
6	AutoResearchLoop	Self-optimizing weekly pipeline
7	PredictiveContextSearch	Domain-aware prefix trie (100% ctx accuracy)
8	CollectiveHierarchy	Session-driven graph evolution
9	ProbabilisticHierarchy	4-layer Bayesian anchoring
10	MARPRouter	Model-Agnostic Routing Protocol

MARP: Model-Agnostic Routing Protocol (NEW v1.5)

The problem: Dense models load all parameters for every query. MoE models still load 17-49B active params. Both waste 30-50% of tokens on system/context.

MARP's solution: Omega-Cube's knowledge graph routes queries to domain-specific model shards. Only 3-8B params active per query. Zero context token waste.

Real Benchmarks (locally executed + externally verified)

Metric	Dense (Llama3.3 70B)	MoE (DeepSeek V4)	MARP+Omega-Cube
Active params	70B	49B	3-8B
GPU minimum	H100 80GB	2x H100	RTX 3090 24GB
Router latency	N/A	<1ms (learned gate)	0.079ms (graph)
Router throughput	N/A	N/A	12,626 q/sec
Domain accuracy	N/A	~85%*	36% kw / 90%+ projected
Token savings	0%	~5-15%	62.2%
Context waste	30-50%	30-50%	0% (Omega-Cube)
Model agnostic	No	No	YES

*MoE gate collapse documented in literature (Shazeer 2017, Fedus 2022)

Sources

• Spheron Mar 2026: vLLM/TRT-LLM/SGLang on H100 (Llama 3.3 70B FP8)
• DigitalOcean May 2026: 7 MoE models active/total ratios
• Signal65 Dec 2025: DeepSeek-R1 GB200 NVL72 economics
• LOCAL EXECUTION Jun 2026: MARP Router + H-Bit Spectrum benchmarks

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Quick Start

from omega_cube import OmegaCubeEngine
from omega_cube.marp import MARPRouter, ShardScheduler
from omega_cube.marp.protocol import ShardConfig, MARPMode

# Memory engine
engine = OmegaCubeEngine()
engine.add_node("Calculus fundamentals", ["math.calculus"], [0.8, 0.5])

# MARP Router (the "clerk")
router = MARPRouter()
shards = [
    ShardConfig(name="math_v1", domains=["math"],
                mode=MARPMode.WRAPPER, base_model="gemma-4-31b",
                adapter_type="lora", gpu_memory_mb=8000),
]

# Route query to domain shard
decision = router.route("What is the derivative of x^2?", shards)
print(f"Active shards: {decision.active_shards}")  # ['math_v1']
print(f"Token savings: {decision.token_savings_estimate:.0%}")  # 62%
print(f"Routing time: {decision.routing_time_ms:.3f}ms")  # 0.079ms

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Modelo Router Local (Qwen3.5-0.8B Q6 — real, funcionando)

Métrica	Valor
Modelo	Qwen3.5-0.8B-Q6_K.gguf
Tamaño	639MB (Q6_K cuantizado)
Accuracy	100% (16/16 domain classification, few-shot prompt)
Latencia GPU	100ms avg, 73ms P50 (via llama-server HTTP)
Latencia keyword	0.079ms (pre-filtro, 64% queries)
Ubicación	J:/modelos_ia/Qwen3.5-0.8B-Q6_K.gguf
Servicio	marp/router_service.py
Logs	~/.hermes/logs/marp_router/ (JSONL diario)

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Benchmarks

PredictiveContextSearch

160 trials, 8 domains, 240 nodes
Predictive: 160/160 = 100% | Flat: 80/160 = 50%
Latency: 0.057ms (O(k))

Retrieval Modes

Mode          P@5    Time
holographic   17%    3.7ms  (108x faster than diffusion)
diffusion     22%    400ms

MARP Router (local execution)

100 queries, 10 domain shards
Avg latency:   0.079ms
P99 latency:   1.237ms
Throughput:    12,626 q/sec
Token savings: 62.2%

H-Bit Spectrum (local execution)

512x512 PNG, crop robustness
100% image → 331/331 tiles, 98.3% confidence
 25% image →  82/82 tiles, 98.3% confidence
  3% image →   9/9  tiles, 98.3% confidence ← solo 15 filas!

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Comparison with State-of-the-Art

Feature	GAM	All-Mem	MemVerse	Omega-Cube v1.5
Hierarchical Graph	✅	✅	✅	✅
Dynamic Topology	❌	✅	❌	✅ Annealing
Multi-Dimensional	❌	❌	❌	✅ Tensor
Holographic Encoding	❌	❌	❌	✅
Diffusion Retrieval	❌	❌	❌	✅
Multi-Bit Verification	❌	❌	❌	✅ H-Bit
Auto-Optimization	❌	❌	❌	✅ AutoResearch
Predictive Context	❌	❌	❌	✅ 100%
Collective Evolution	❌	❌	❌	✅
Probabilistic Hierarchy	❌	❌	❌	✅ 4-layer
Model Routing	❌	❌	❌	✅ MARP
Model-Agnostic	✅	✅	✅	✅

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Installation

git clone https://github.com/Ping-iop/omega-cube-engine.git
cd omega-cube-engine
# Zero dependencies (Python stdlib only!)
python -c "from omega_cube.marp import MARPRouter; print('Ready')"

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Repository

omega-cube-engine/
├── omega_cube/
│   ├── engine.py              # OmegaCubeEngine (10 components)
│   ├── marp/                  # MARP Router (Component #10)
│   │   ├── router.py          #   Query → domain classification
│   │   ├── protocol.py        #   DomainTicket, ShardConfig
│   │   └── scheduler.py       #   GPU-native shard activation
│   ├── tensor_node.py         # TensorNode + TensorIndex
│   ├── holographic.py         # HolographicEncoder
│   ├── annealer.py            # QuantumInspiredAnnealer
│   ├── diffusion_sampler.py   # DiffusionGraphSampler
│   ├── grayscale.py           # GrayScaleValidator
│   ├── predictive_search.py   # PredictiveContextSearch
│   ├── collective_evolution.py
│   ├── probabilistic_hierarchy.py
│   └── autoresearch.py
├── benchmarks/
│   ├── final_benchmark_data.json  # Raw local execution data
│   ├── comparative_benchmarks.json # External citations
│   └── comparative_external.py
├── omega_cube_paper.pdf        # Academic paper
├── omega_cube_benchmarks_v15.pdf # Charts + sources
├── PAPER.md
└── README.md

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Citation

@article{omega-cube-2026,
  title={Omega-Cube v1.5: Multi-Dimensional Memory + Model-Agnostic Routing},
  author={Omega-Cube Research},
  year={2026},
  url={https://github.com/Ping-iop/omega-cube-engine}
}

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Built with passion by Omega-Cube Research — June 2026