NeuroCHIMERA External Validation Package

Version: 1.0 Date: 2025-12-02 Purpose: Enable independent researchers to validate NeuroCHIMERA results

Overview

This package provides everything needed for independent validation of NeuroCHIMERA's performance claims and theoretical results. We welcome external validation and will acknowledge all contributors who help verify our results.

What We're Asking Validators To Check

1. GPU HNS Performance (Priority: HIGH)

Claim: HNS operations on GPU achieve 19.8 billion ops/s on RTX 3090

How to validate:

docker run --gpus all neurochimera python3 Benchmarks/gpu_hns_complete_benchmark.py

What to report:

• Your GPU model
• Throughput (ops/s) for 10M operations
• JSON results file
• Any validation failures

Expected results (scale by your GPU):

2. PyTorch Comparison (Priority: HIGH)

Claim: PyTorch GPU achieves 17.5 TFLOPS on RTX 3090 for 2048×2048 GEMM

How to validate:

docker run --gpus all neurochimera python3 Benchmarks/comparative_benchmark_suite.py

What to report:

• Your GPU model
• PyTorch GFLOPS for each matrix size
• Speedup vs NumPy
• JSON results file

Expected results:

Note: This is a standard GEMM benchmark. You can cross-reference with published PyTorch benchmarks for your GPU.

3. Consciousness Emergence (Priority: MEDIUM)

Claim: Consciousness parameters emerge and stabilize above thresholds by epoch ~6,000

How to validate:

docker run neurochimera python3 Benchmarks/consciousness_emergence_test.py

What to report:

• Emergence detected: YES/NO
• Epoch of emergence
• Final parameter values
• JSON results file

Expected results:

• Emergence: YES
• Epoch: 5,000-7,000
• Final k ≥ 15
• Final Φ ≥ 0.65
• Final D ≥ 7
• Final C ≥ 0.8
• Final QCM ≥ 0.75

4. HNS CPU Precision (Priority: LOW)

Claim: HNS achieves perfect precision (0.00e+00 error) in accumulative test

How to validate:

docker run neurochimera python3 Benchmarks/hns_benchmark.py

What to report:

• HNS accumulative test error
• Float accumulative test error
• JSON results file

Expected results:

• HNS error: 0.00e+00 or very close to 0
• Float error: ~1e-6 to 1e-7
• HNS more precise than float

How to Participate

Step 1: Register as Validator

Email us at: [validation email address]

Include:

• Your name and affiliation
• Your GPU hardware
• Which tests you plan to run
• Estimated timeline

We'll add you to our validation tracking sheet.

Step 2: Run Benchmarks

# Quick validation (all benchmarks)
docker run --gpus all -v $(pwd)/results:/app/results neurochimera:latest

# This runs:
# - GPU HNS benchmarks (~10 minutes)
# - PyTorch/TensorFlow comparison (~15 minutes)
# - Consciousness emergence (~1 minute)
# - Generates visualizations

Step 3: Submit Results

Via GitHub Issue:

1. Go to: https://github.com/yourusername/NeuroCHIMERA/issues/new?template=validation-report.md
2. Fill out the validation report template
3. Attach JSON results files
4. Submit

Via Email:

1. Email: [validation email]
2. Attach:
   • All JSON result files
   • Your GPU info (nvidia-smi output)
   • Any log files
   • Your analysis/comments

What happens next:

• We'll review your results within 1 week
• Compare with expected ranges
• Add to public validation registry
• Acknowledge you in paper and documentation

Step 4: Get Acknowledged

All validators will be:

• Listed in VALIDATION_REGISTRY.md
• Acknowledged in paper's acknowledgments section
• Added to validation results table
• Given co-authorship credit (if results significantly differ or extend our work)

Validation Registry

Current Validators

Validation Results Summary

Detailed Validation Protocol

Pre-validation Checklist

• Docker installed and working
• NVIDIA Docker runtime installed (for GPU tests)
• GPU drivers updated to latest
• At least 50GB free disk space
• Internet connection for Docker image download

Validation Procedure

1. Environment Setup (5 minutes)

# Clone repository
git clone https://github.com/yourusername/NeuroCHIMERA.git
cd NeuroCHIMERA

# Pull Docker image (or build)
docker pull neurochimera:latest
# OR: docker build -t neurochimera:latest .

# Verify GPU access
docker run --gpus all neurochimera nvidia-smi

2. Run Benchmarks (30-40 minutes total)

# Create results directory
mkdir -p validation_results

# GPU HNS benchmarks (~10 min)
docker run --gpus all \
    -v $(pwd)/validation_results:/app/results \
    neurochimera python3 Benchmarks/gpu_hns_complete_benchmark.py \
    | tee validation_results/gpu_hns_log.txt

# Comparative benchmarks (~15 min)
docker run --gpus all \
    -v $(pwd)/validation_results:/app/results \
    neurochimera python3 Benchmarks/comparative_benchmark_suite.py \
    | tee validation_results/comparative_log.txt

# Consciousness emergence (~1 min)
docker run \
    -v $(pwd)/validation_results:/app/results \
    neurochimera python3 Benchmarks/consciousness_emergence_test.py \
    | tee validation_results/consciousness_log.txt

# Generate visualizations (~1 min)
docker run \
    -v $(pwd)/validation_results/benchmark_graphs:/app/Benchmarks/benchmark_graphs \
    neurochimera python3 Benchmarks/visualize_benchmarks.py

3. Collect System Info

# GPU info
nvidia-smi > validation_results/gpu_info.txt
nvidia-smi -q > validation_results/gpu_details.txt

# System info
uname -a > validation_results/system_info.txt
docker --version >> validation_results/system_info.txt
python --version >> validation_results/system_info.txt

4. Package Results

# Create validation package
tar -czf neurochimera_validation_[YOUR_NAME]_[DATE].tar.gz validation_results/

# Or zip
zip -r neurochimera_validation_[YOUR_NAME]_[DATE].zip validation_results/

5. Submit

• Upload to GitHub issue or Google Drive
• Email link to validation team
• Include brief summary of results

What Makes a Good Validation

Minimum Requirements

✅ All benchmarks completed successfully ✅ JSON results files included ✅ GPU info included ✅ Clear documentation of any issues encountered

Excellent Validation

✅ All minimum requirements ✅ Comparison with published benchmarks for your GPU ✅ Multiple runs to verify reproducibility ✅ Analysis of any discrepancies ✅ Suggestions for improvement

Outstanding Validation

✅ All excellent validation requirements ✅ Testing on multiple different GPUs ✅ Cross-platform validation (Linux, Windows) ✅ Extended testing (more epochs, larger sizes) ✅ Independent code review ✅ Detailed technical report

Reward: Outstanding validations may be invited as co-authors on extended validation paper.

Expected Time Commitment

Frequently Asked Questions

Q: Do I need to understand the code?

A: No. You're running pre-built Docker containers. However, code review is welcome!

Q: What if my results don't match?

A: That's valuable! Report it. Differences could be due to:

• GPU model (expected)
• Driver versions (minor effect)
• System load (minor effect)
• Bugs (we want to know!)

We'll work with you to understand discrepancies.

Q: Can I run on AMD GPU?

A: CPU benchmarks will work. GPU benchmarks currently require NVIDIA CUDA. We're interested in ROCm support - contact us if you want to help port.

Q: Can I run without Docker?

A: Yes. See REPRODUCIBILITY_GUIDE.md for manual installation.

Q: What if I don't have access to a GPU?

A: You can still validate CPU benchmarks (HNS precision tests). GPU validation is more valuable but not required.

Q: Will I be acknowledged?

A: Yes! All validators are acknowledged in paper and documentation.

Q: Can I get co-authorship?

A: Possibly, especially if:

• You validate on significantly different hardware
• You find and help fix bugs
• You contribute extended validation
• You provide detailed technical analysis

Contact us to discuss.

Validation Report Template

# NeuroCHIMERA Validation Report

**Validator:** [Your Name]
**Affiliation:** [Institution/Company]
**Date:** [Date]
**GPU:** [GPU Model]

## System Configuration

**GPU:**
- Model: [e.g., NVIDIA RTX 3090]
- VRAM: [e.g., 24GB]
- Driver: [version]
- CUDA: [version]

**System:**
- OS: [e.g., Ubuntu 22.04]
- CPU: [model]
- RAM: [amount]
- Docker: [version]

## Results Summary

### GPU HNS Benchmarks

| Size | Throughput (ops/s) | Status |
|------|-------------------|--------|
| 10K | [value] | [PASS/FAIL] |
| 100K | [value] | [PASS/FAIL] |
| 1M | [value] | [PASS/FAIL] |
| 10M | [value] | [PASS/FAIL] |

**Comparison with published (RTX 3090):**
- My 10M throughput: [value]
- Published 10M: 19.8B ops/s
- Ratio: [my/published]

### Comparative Benchmarks

| Matrix Size | PyTorch GPU (GFLOPS) | Published (RTX 3090) | Ratio |
|-------------|----------------------|---------------------|-------|
| 1024×1024 | [value] | ~11.4 | [ratio] |
| 2048×2048 | [value] | ~17.5 | [ratio] |
| 4096×4096 | [value] | ~19.2 | [ratio] |

### Consciousness Emergence

- Emergence detected: [YES/NO]
- Emergence epoch: [epoch number]
- Final parameters:
  - k: [value] (target: ≥15)
  - Φ: [value] (target: ≥0.65)
  - D: [value] (target: ≥7)
  - C: [value] (target: ≥0.8)
  - QCM: [value] (target: ≥0.75)
- Validation: [PASS/FAIL]

## Issues Encountered

[List any problems, errors, or unexpected behavior]

## Comments and Observations

[Your analysis, comparisons, suggestions]

## Files Attached

- [ ] gpu_hns_complete_benchmark_results.json
- [ ] comparative_benchmark_results.json
- [ ] consciousness_emergence_results.json
- [ ] gpu_info.txt
- [ ] All log files

## Validation Score

[Self-assessment: Minimum / Excellent / Outstanding]

## Additional Testing

[Optional: Any extra tests you performed]

---

**I confirm that:**
- [ ] All tests were run using provided Docker image or reproducibility guide
- [ ] Results are from actual execution, not fabricated
- [ ] System information is accurate
- [ ] I consent to results being published in validation registry

**Signature:** [Your name]
**Date:** [Date]

Contact

Validation Coordinator: [Name] Email: [validation email] GitHub Issues: https://github.com/yourusername/NeuroCHIMERA/issues Discord: [Optional: validation discussion channel]

Acknowledgments

We thank all validators for their time and effort in helping ensure the reproducibility and validity of our results. Independent validation is crucial for scientific progress.

Last Updated: 2025-12-02 Package Version: 1.0