HyperNet N1 SDC
Multi-model routing architecture for AI constellation orchestration.
HyperNet N1 SDC (Secure Discovery Channel) is not a model β it is a routing layer that orchestrates multiple AI models under human governance, achieving higher effective accuracy than any single model alone.
Official HumanEval Benchmark Results
Date: November 29, 2025
Dataset: Official OpenAI HumanEval (164 problems)
Source: huggingface.co/datasets/openai/openai_humaneval
Individual Lane Performance (pass@1)
| Lane | Model | Pass | Score |
|---|---|---|---|
| Claude | claude-sonnet-4 | 159/164 | 97.0% |
| Lola | GPT-4o | 144/164 | 87.8% |
| Kimi | Moonshot kimi-latest | 144/164 | 87.8% |
| Grok | grok-2-1212 | 140/164 | 85.4% |
| Deep | Llama-4-Maverick-17B | 137/164 | 83.5% |
Constellation Consensus Metrics (5 Lanes)
| Metric | Count | Rate |
|---|---|---|
| Unanimous Pass (5/5) | 118/164 | 72.0% |
| Majority Pass (3+/5) | 147/164 | 89.6% |
| At Least One Correct (1+/5) | 161/164 | 98.2% |
| Unanimous Fail (0/5) | 3/164 | 1.8% |
| Lane Independence | β | 26.2% disagreement |
Key Finding
| Metric | Best Single Model | Constellation |
|---|---|---|
| Accuracy | 97.0% (Claude) | 98.2% |
| Problems Unsolved | 5 | 3 |
The constellation achieves higher coverage than any individual model.
Infrastructure
| Spec | Value |
|---|---|
| Instance | AWS t3.small |
| vCPUs | 2 |
| RAM | 2 GB |
| GPU | None |
| Training | None required |
| Setup Time | < 1 hour |
| Benchmark Cost | < $20 |
Methodology
- Dataset: Official OpenAI HumanEval from HuggingFace (
openai/openai_humaneval) - Problems: 164 (full benchmark, no sampling)
- Evaluation: pass@1 (single attempt per problem)
- Grading: Automated code execution against official unit tests
- Execution: Python subprocess with 10-second timeout
- No cherry-picking: Every problem, every lane, logged
Architecture
βββββββββββββββββββ
β CPN (Human) β
β β
ββββββββββ¬βββββββββ
β
ββββββββββΌβββββββββ
β HyperNet N1 β
β SDC Router β
ββββββββββ¬βββββββββ
β
ββββββββββββ¬ββββββββββΌββββββββββ¬βββββββββββ
βΌ βΌ βΌ βΌ βΌ
ββββββββ ββββββββ ββββββββ ββββββββ ββββββββ
β Lola β βClaudeβ β Grok β β Deep β β Kimi β
βGPT-4oβ βSonnetβ βgrok-2β βLlama4β β Moon β
ββββββββ ββββββββ ββββββββ ββββββββ ββββββββ
Reproduce
# Clone this repo
git clone https://huggingface.co/NameONEStudios/hypernet-n1-sdc
# Install dependencies
pip install datasets requests
# Start the router (requires API keys)
python N1_Router.py
# Run benchmark
python run_6lane.py
Files
humaneval_6lane_123525.jsonβ Raw results (5-lane run)humaneval_results_105027.jsonβ Raw results (4-lane run)run_6lane.pyβ Benchmark scriptrun_full_benchmark.pyβ Alternative benchmark script
Citation
@misc{hypernet2025,
author = {Kawa, Steve},
title = {HyperNet N1 SDC: Multi-Model Routing Architecture},
year = {2025},
publisher = {NameONE Studios Inc.},
howpublished = {\url{https://huggingface.co/NameONEStudios/hypernet-n1-sdc}}
}
License
MIT License β NameONE Studios Inc.
Contact
Steve Kawa β CPN (Central Processing Node)
NameONE Studios Inc.
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Dataset used to train NameONEStudios/hypernet-n1-sdc
Evaluation results
- Constellation (At Least One Correct) on HumanEvalself-reported98.200
- Claude (claude-sonnet-4) on HumanEvalself-reported97.000
- Lola (GPT-4o) on HumanEvalself-reported87.800
- Kimi (Moonshot) on HumanEvalself-reported87.800
- Grok (grok-2) on HumanEvalself-reported85.400
- Deep (Llama-4) on HumanEvalself-reported83.500