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language: en
license: mit
tags:
- code-generation
- multi-model
- routing
- humaneval
- constellation
- orchestration
datasets:
- openai/openai_humaneval
metrics:
- pass@1
model-index:
- name: HyperNet N1 SDC
results:
- task:
type: code-generation
name: Code Generation
dataset:
type: openai/openai_humaneval
name: HumanEval
metrics:
- type: pass@1
value: 98.2
name: Constellation (At Least One Correct)
- type: pass@1
value: 97.0
name: Claude (claude-sonnet-4)
- type: pass@1
value: 87.8
name: Lola (GPT-4o)
- type: pass@1
value: 87.8
name: Kimi (Moonshot)
- type: pass@1
value: 85.4
name: Grok (grok-2)
- type: pass@1
value: 83.5
name: Deep (Llama-4)
---
# 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
```bash
# 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.json` β Raw results (5-lane run)
- `humaneval_results_105027.json` β Raw results (4-lane run)
- `run_6lane.py` β Benchmark script
- `run_full_benchmark.py` β Alternative benchmark script
## Citation
```bibtex
@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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