WrinkleBrane - Experimental Wave-Interference Memory

⚠️ Research Prototype ⚠️

Early-stage experimental system requiring significant validation

Model Description

WrinkleBrane is a novel experimental approach to associative memory that uses wave-interference patterns in tensor operations. The system stores information in stacked 2D "membranes" and retrieves it through parallel vertical slicing operations, demonstrating promising initial results on controlled test cases.

This is a research prototype with significant limitations and is not ready for production use.

Model Details

Model Type: Experimental associative memory system
Architecture: Wave-interference tensor operations with orthogonal code spaces
Framework: PyTorch
License: AGPL-3.0
Research Status: Early experimental phase requiring extensive validation

Key Innovation

Wave-interference memory: Novel tensor-based approach using constructive/destructive interference patterns
Orthogonal code spaces: Hadamard and DCT codes for minimal cross-talk
Parallel retrieval: Single-pass access to all stored patterns via tensor slicing
Mathematical foundation: Theoretically grounded in linear algebra and signal processing

Intended Uses

Research Applications ✅

Academic research in associative memory systems
Algorithm development and interference pattern studies
Proof-of-concept demonstrations for wave-based memory
Educational purposes for understanding tensor interference

Not Suitable For ❌

Production applications (requires extensive validation)
Large-scale deployment (scalability unproven)
Mission-critical systems (reliability not established)
Commercial use without significant additional development

Performance and Limitations

Demonstrated Capabilities

High precision: 150+ dB PSNR on simple geometric test patterns
Theoretical consistency: Behavior matches mathematical predictions
Orthogonal performance: Excellent results with Hadamard codes (zero cross-correlation)
Modular design: Clean, well-structured PyTorch implementation

Critical Limitations

Limited testing: Only validated on simple synthetic patterns
No baselines: No comparison to standard associative memory approaches
Scale uncertainty: Performance at practical scales unknown
Dataset restriction: Not tested on realistic, complex data

Known Issues

Testing limited to controlled laboratory conditions
Theoretical capacity limits unconfirmed on real-world data
No robustness analysis under noise or interference
Long-term stability and persistence mechanisms underdeveloped

Technical Specifications

Core Operations

# Write Operation: Store pattern with interference
M += Σᵢ αᵢ · C[:, kᵢ] ⊗ Vᵢ

# Read Operation: Parallel retrieval via tensor slicing  
Y = ReLU(einsum('blhw,lk->bkhw', M, C) + b)

Tensor Shapes

Membranes: M ∈ ℝ[B, L, H, W] - 4D tensor storage
Codes: C ∈ ℝ[L, K] - Orthogonal slice weights
Patterns: V ∈ ℝ[H, W] - 2D information maps
Output: Y ∈ ℝ[B, K, H, W] - Retrieved patterns

Requirements

PyTorch 2.0+
NumPy
SciPy (for code generation)
Matplotlib (for visualization)

Getting Started

Installation

git clone https://huggingface.co/WCNegentropy/WrinkleBrane
cd WrinkleBrane
pip install -r requirements.txt

Basic Usage

from wrinklebrane.membrane_bank import MembraneBank
from wrinklebrane.codes import hadamard_codes
from wrinklebrane.write_ops import store_pairs
from wrinklebrane.slicer import make_slicer

# Setup memory system
B, L, H, W, K = 1, 64, 32, 32, 8
bank = MembraneBank(L=L, H=H, W=W)
bank.allocate(B)

# Generate orthogonal codes
C = hadamard_codes(L, K)
slicer = make_slicer(C)

# Store patterns
keys = [0, 1, 2]  
values = [pattern1, pattern2, pattern3]  # H×W tensors
alphas = [1.0, 1.0, 1.0]

store_pairs(bank.M, C, keys, values, alphas)

# Retrieve all patterns
retrieved = slicer(bank.M[0])  # Shape: [K, H, W]

Running Experiments

# Basic associative memory test
python experiments/p0_assoc_mem.py --L 64 --H 32 --W 32 --K 8 --codes hadamard

# Performance benchmarking
python performance_benchmark.py

# Comprehensive testing
python comprehensive_test.py

Research Status and Validation Needs

Current Research Phase

WrinkleBrane is in early experimental development. While demonstrating promising technical concepts, extensive validation is required before practical applications.

Required Validation Work

High Priority:

Systematic comparison to established associative memory baselines
Evaluation on realistic datasets (MNIST, CIFAR, natural images)
Statistical analysis with multiple runs and confidence intervals
Large-scale performance and robustness studies

Medium Priority:

Noise robustness characterization
Failure mode analysis and system limitations
Computational efficiency benchmarking vs alternatives
Long-term stability and persistence validation

Honest Assessment

What WrinkleBrane Demonstrates:

✅ Novel tensor-based interference memory approach
✅ High precision on controlled test patterns
✅ Mathematical consistency with theoretical predictions
✅ Clean, modular implementation with test coverage

What Remains Unproven:

❓ Performance on realistic, complex datasets
❓ Competitive advantages over existing approaches
❓ Scalability to practically relevant problem sizes
❓ Robustness under real-world conditions

Citation

If you use WrinkleBrane in your research, please cite:

@software{wrinklebrane2025,
  title={WrinkleBrane: Experimental Wave-Interference Memory},
  author={WCNegentropy Holdings LLC},
  year={2025},
  url={https://huggingface.co/WCNegentropy/WrinkleBrane},
  note={Research prototype - experimental software}
}

Documentation

📋 Research Status - Current limitations and validation needs
🔬 Assessment Report - Experimental results and analysis
⚡ Optimization Analysis - Performance characteristics
🛠️ Development Workflow - Testing and development procedures

License and Disclaimer

This software is released under the AGPL-3.0 license.

EXPERIMENTAL SOFTWARE DISCLAIMER: WrinkleBrane is experimental research software. It has not been validated for production use and may contain bugs, limitations, or unexpected behaviors. Use at your own risk and conduct thorough testing before any practical applications.

Contributing

We welcome contributions to help validate and improve WrinkleBrane:

Research validation: Comparative studies and benchmarking
Testing: Evaluation on new datasets and use cases
Optimization: Performance improvements and scaling studies
Documentation: Improved explanations and tutorials

Please see our research status document for priority validation needs.

WrinkleBrane represents early-stage research in wave-interference memory systems. While showing promise, it requires substantial additional validation before practical applications.

WCNegentropy
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WrinkleBrane