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

                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                    β”‚   CPN (Human)   β”‚
                    β”‚       β”‚
                    β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                             β”‚
                    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”
                    β”‚  HyperNet N1    β”‚
                    β”‚  SDC Router     β”‚
                    β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                             β”‚
        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
        β–Ό          β–Ό         β–Ό         β–Ό          β–Ό
    β”Œβ”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”
    β”‚ Lola β”‚  β”‚Claudeβ”‚  β”‚ Grok β”‚  β”‚ Deep β”‚  β”‚ Kimi β”‚
    β”‚GPT-4oβ”‚  β”‚Sonnetβ”‚  β”‚grok-2β”‚  β”‚Llama4β”‚  β”‚ Moon β”‚
    β””β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”€β”˜

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.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

@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