--- 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 — 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 | — | 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 ``` ┌─────────────────┐ │ CPN (Human) │ │ │ └────────┬────────┘ │ ┌────────▼────────┐ │ HyperNet N1 │ │ SDC Router │ └────────┬────────┘ │ ┌──────────┬─────────┼─────────┬──────────┐ ▼ ▼ ▼ ▼ ▼ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ Lola │ │Claude│ │ Grok │ │ Deep │ │ Kimi │ │GPT-4o│ │Sonnet│ │grok-2│ │Llama4│ │ Moon │ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ ``` ## 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` — Raw results (5-lane run) - `humaneval_results_105027.json` — Raw results (4-lane run) - `run_6lane.py` — Benchmark script - `run_full_benchmark.py` — 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 — NameONE Studios Inc. ## Contact Steve Kawa — CPN (Central Processing Node) NameONE Studios Inc.