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NeuroCHIMERA Benchmark Disclaimer and Transparency Statement

NeuroCHIMERA Benchmark Disclaimer and Transparency Statement

Date: 2025-12-01 Version: 1.0

Purpose of This Document

This document provides complete transparency about the validation status of all performance claims in the NeuroCHIMERA project. We distinguish between experimentally validated data, theoretical projections, and placeholder values to ensure scientific integrity and enable independent verification.

Validation Status Legend

SymbolMeaningDescription
✅ValidatedExperimentally measured with JSON backing, reproducible
⚠️PartialMeasured but has issues or inconsistencies requiring correction
📊TheoreticalBased on projections or models, not yet experimentally validated
❌InvalidTest failed or data found to be incorrect
📋PendingPlanned but not yet executed

Benchmark Claims Status

Core System Performance

Evolution Speed ✅ VALIDATED

Claim: "8-12M neurons/s evolution speed"

Status: ✅ Fully validated

Evidence:

  • JSON file: Benchmarks/system_benchmark_results.json
  • Multiple test sizes: 65K, 262K, 1M neurons
  • Consistent results across runs

Reproducibility:

python Benchmarks/benchmark_neurochimera_system.py

Confidence: High - Independently reproducible

GPU Compute Performance ✅ VALIDATED

Claim: "0.21-0.31 GFLOPS on RTX 3090"

Status: ✅ Fully validated

Evidence:

  • JSON file: Benchmarks/gpu_complete_system_benchmark_results.json
  • Tested configurations: 65K, 262K, 1M neurons
  • Real GPU measurements

Reproducibility:

python Benchmarks/benchmark_gpu_complete_system.py

Confidence: High - Hardware-specific but reproducible

Hierarchical Number System (HNS)

HNS CPU Speed ⚠️ VALIDATED WITH ISSUES

Claim: "~25x slower than float on CPU"

Status: ⚠️ Data exists but claim is incorrect

Reality: ~200x slower (214.76x for addition, 201.60x for scaling)

Evidence:

  • JSON file: Benchmarks/hns_benchmark_results.json
  • Actual overhead measurements available
  • Documentation requires correction

Issue: Reports claim "25x" but JSON shows "200x"

Action Required: Update all documentation references from "25x" to "200x"

Reproducibility:

python Benchmarks/hns_benchmark.py

Confidence: High for measurements, Low for current documentation

HNS CPU Accumulative Test ❌ FAILED

Claim: "Maintains same precision as float in accumulation"

Status: ❌ Test failed

Reality: Test result = 0.0 (100% error)

Evidence:

  • JSON file: Benchmarks/hns_benchmark_results.json (lines 52-66)
  • Clear failure in accumulative test
  • Indicates implementation bug or measurement error

Action Required:

  1. Debug accumulation logic
  2. Fix implementation
  3. Re-run test
  4. Remove precision claims until validated

Reproducibility:

python Benchmarks/hns_benchmark.py  # Will show failure

Confidence: High for failure detection, requires fix

HNS GPU Performance 📋 PENDING VALIDATION

Claim: "HNS is 1.21x faster than float in addition on GPU"

Status: 📋 Pending validation - No JSON backing

Reality: Claim exists in report, no data file found

Evidence:

  • Report: Benchmarks/GPU_BENCHMARK_REPORT.md
  • JSON file: MISSING
  • Needs re-execution with proper data logging

Action Required:

  1. Re-run GPU HNS benchmarks
  2. Save JSON results
  3. Verify claims or correct

Reproducibility:

python Benchmarks/hns_gpu_benchmark.py  # Needs verification

Confidence: Low - Unvalidated claim

Optimization Performance

Optimization Speedup ⚠️ INCONSISTENT

Claim: "65x faster after optimization"

Status: ⚠️ Data exists but shows different value

Reality: JSON shows 16x speedup, not 65x

Evidence:

  • JSON file: Benchmarks/optimized_gpu_benchmark_results.json
  • Measured speedup: 15.963884x
  • Report claims: 65x

Issue: 4x discrepancy between report and data

Possible Explanations:

  1. Different test configurations
  2. Confusion between different metrics
  3. Error in report generation

Action Required:

  1. Verify source of 65x claim
  2. If valid, clarify which configuration
  3. Otherwise, correct to 16x

Reproducibility:

python Benchmarks/benchmark_optimized_gpu.py

Confidence: High for 16x measurement, Low for 65x claim

Comparative Benchmarks

PyTorch Comparison 📊 THEORETICAL

Claim: "43× speedup over PyTorch"

Status: 📊 Theoretical projection - No actual benchmark

Reality: No PyTorch comparison executed

Evidence:

  • README shows comparison table
  • No JSON file: comparative_benchmark_results.json not found
  • Script exists but no output

Nature: Theoretical projection based on operation counts

Action Required:

  1. Run actual PyTorch benchmarks
  2. Save JSON results
  3. Update table with real data or mark as theoretical

Reproducibility:

python Benchmarks/benchmark_comparative.py  # Needs PyTorch installed

Confidence: None - Not yet measured

Disclaimer: ⚠️ This claim is a theoretical projection, not an experimental measurement. Independent validation required.

Memory Efficiency 📊 PARTIALLY VALIDATED

Claim: "88.7% memory reduction"

Status: 📊 Theoretical calculation, partial validation

Reality: Some memory measurements exist, full study needed

Evidence:

  • Partial data in system_benchmark_results.json
  • Memory efficiency calculations available
  • Comprehensive profiling needed

Action Required:

  1. Run comprehensive memory profiling
  2. Multiple scales (10^6 to 10^9 neurons)
  3. Compare with PyTorch equivalent networks

Confidence: Medium - Partial validation, needs completion

Known Issues Summary

Critical Issues (Must Fix)

  1. HNS Accumulative Test Failure

    • Status: ❌ Failed
    • Impact: Questions HNS functionality
    • Priority: P0
    • ETA: 1 week

  2. Documentation Discrepancies

    • Status: ⚠️ Multiple inconsistencies
    • Impact: Scientific credibility
    • Priority: P0
    • ETA: 3-5 days

High Priority Issues

  1. GPU HNS Validation Missing

    • Status: 📋 Pending
    • Impact: Unvalidated claims
    • Priority: P1
    • ETA: 1 week

  2. PyTorch Comparison Not Run

    • Status: 📊 Theoretical only
    • Impact: Key comparison unvalidated
    • Priority: P1
    • ETA: 1 week

  3. CPU Overhead Misreported

    • Status: ⚠️ 200x not 25x
    • Impact: Misleading expectations
    • Priority: P1
    • ETA: 1 day (documentation only)

Reproducibility Guide

System Requirements

Hardware:

  • GPU: OpenGL 4.3+ compatible (NVIDIA RTX series recommended)
  • VRAM: 4GB minimum, 8GB+ recommended for large networks
  • CPU: Modern multi-core processor
  • RAM: 16GB+ recommended

Software:

  • Python 3.8+
  • moderngl >= 5.6.0
  • numpy >= 1.19.0
  • PyTorch >= 1.9.0 (for comparative benchmarks)

Running Benchmarks

1. System Benchmarks (Validated ✅)

# Evolution speed benchmarks
python Benchmarks/benchmark_neurochimera_system.py

# GPU complete system benchmarks
python Benchmarks/benchmark_gpu_complete_system.py

# Expected output: JSON files in Benchmarks/ directory
# Results should match within ±10% due to hardware variation

2. HNS Benchmarks (Partial ⚠️)

# CPU HNS benchmarks (note: accumulative test will fail)
python Benchmarks/hns_benchmark.py

# GPU HNS benchmarks (needs re-validation)
python Benchmarks/hns_gpu_benchmark.py

# Expected: JSON files, note accumulative test failure

3. Optimization Benchmarks (Needs verification ⚠️)

# Optimized GPU benchmarks
python Benchmarks/benchmark_optimized_gpu.py

# Expected speedup: ~16x (not 65x as reported in some docs)

4. Comparative Benchmarks (Pending 📋)

# PyTorch comparison (requires PyTorch installation)
pip install torch torchvision
python Benchmarks/benchmark_comparative.py

# Currently: No output JSON, needs implementation

Expected Variation

Normal variation in results:

  • ±5-10% for throughput measurements (GPU-dependent)
  • ±2-5% for timing measurements (system load-dependent)
  • Exact match for precision tests

Hardware-specific results:

  • Different GPUs will show different absolute performance
  • Relative speedups (XxX) should be consistent
  • Memory usage should scale linearly

Validation Methodology

Our Standards

For a claim to be marked "Validated ✅":

  1. ✅ Raw data saved: JSON file with measurements
  2. ✅ Multiple runs: Minimum 10 iterations
  3. ✅ Statistical analysis: Mean and standard deviation reported
  4. ✅ Configuration documented: Hardware, drivers, OS versions
  5. ✅ Reproducible: Scripts + instructions provided
  6. ✅ Timestamp: Date and system state recorded

If any criterion is missing: Claim marked as Partial ⚠️ or Pending 📋

Independent Validation Welcome

We actively encourage independent researchers to:

  • Run our benchmarks on your hardware
  • Report discrepancies or issues
  • Suggest improvements to methodology
  • Share your results publicly

How to contribute:

  1. Run benchmarks following this guide
  2. Compare your results with our claims
  3. Report findings (GitHub issues or direct contact)
  4. We will acknowledge and incorporate feedback

Scientific Integrity Statement

Our Commitment

We commit to:

  1. Transparency: Clearly distinguish validated from theoretical
  2. Accuracy: Correct errors promptly when found
  3. Reproducibility: Provide all tools for independent verification
  4. Humility: Acknowledge limitations and failures openly
  5. Collaboration: Welcome community validation efforts

What We've Done

  • ✅ Complete audit of all benchmark claims
  • ✅ Identified and documented all discrepancies
  • ✅ Created detailed validation status for each claim
  • ✅ Provided reproducibility instructions
  • ✅ Invited independent validation

What We're Doing

  • 🔄 Fixing HNS accumulative test
  • 🔄 Correcting documentation discrepancies
  • 🔄 Re-running GPU HNS benchmarks
  • 🔄 Executing PyTorch comparisons
  • 🔄 Adding statistical significance to all tests

What We'll Do

  • 📋 Provide comprehensive reproduction package
  • 📋 Submit raw data as supplementary material
  • 📋 Update all documentation with validated data
  • 📋 Maintain this disclaimer until all validation complete

Timeline for Complete Validation

Phase 1: Fix Critical Issues (1-2 weeks)

  • Fix HNS accumulative test
  • Correct overhead claims (25x → 200x)
  • Re-run GPU HNS benchmarks

Phase 2: Complete Missing Benchmarks (3-4 weeks)

  • Execute PyTorch comparisons
  • Run consciousness emergence tests
  • Add statistical significance

Phase 3: Independent Validation (6-8 weeks)

  • Share with external researchers
  • Collect feedback and results
  • Address any discrepancies

Target: Complete validation by Q2 2025

How to Interpret This Project's Claims

When Reading Documentation

Look for validation markers:

  • ✅ Green check: Experimentally validated, trust with confidence
  • ⚠️ Warning: Data exists but has issues, verify independently
  • 📊 Chart: Theoretical projection, treat as hypothesis
  • ❌ Red X: Known issue, do not rely on claim
  • 📋 Clipboard: Planned but not done, future work

If no marker: Assume pending validation until verified

When Citing This Work

Safe to cite (validated):

  • System evolution performance (8-12M neurons/s)
  • GPU compute performance (0.21-0.31 GFLOPS)
  • Optimization improvements (16x speedup)
  • Architecture and methodology

Cite with caution (pending validation):

  • HNS GPU performance (needs re-validation)
  • PyTorch comparisons (theoretical projection)
  • Memory efficiency (partial validation)

Do not cite (known issues):

  • HNS accumulative precision (test failed)
  • CPU overhead as "25x" (actually 200x)
  • Optimization as "65x" (actually 16x)

For Peer Review

Validated claims suitable for peer review:

  • Core system architecture and implementation
  • Validated performance benchmarks
  • Consciousness monitoring framework
  • Theoretical foundations

Claims requiring validation before peer review:

  • HNS precision advantages
  • Comparative performance (PyTorch)
  • Consciousness emergence observations

Contact for Validation Questions

Project Lead: Francisco Angulo de Lafuente

Theoretical Framework: V.F. Veselov

  • Moscow Institute of Electronic Technology (MIET)

Independent Validation Submissions: Please create GitHub issues with:

  • Your system configuration
  • Benchmark results (JSON files)
  • Comparison with our claims
  • Any discrepancies found

We will respond to all validation inquiries within 48-72 hours.

Conclusion

This disclaimer ensures complete transparency about the validation status of all NeuroCHIMERA performance claims. We prioritize scientific integrity over marketing appeal and welcome independent validation.

Current Status:

  • ✅ Core functionality: Validated
  • ⚠️ Some performance claims: Require correction
  • 📋 Comparative benchmarks: Pending execution
  • 🔄 Active improvement: Fixing all identified issues

Recommendation: Treat validated claims (✅) with confidence, verify partial claims (⚠️) independently, and await completion of pending benchmarks (📋) for full assessment.

Last Updated: 2025-12-01 Next Review: 2025-12-08 Version: 1.0

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