Holographic_Neural_Mesh

Holographic Neural Mesh (HNM) v3.0

A deterministic sparse semantic substrate for cognitive systems. No GPU required.

Model Description

HNM is not a language model or embedding model replacement. It is a cognitive memory layer providing:

• ✅ Constant-time encoding (O(1) regardless of corpus size)
• ✅ 99% structural sparsity (102× FLOPS reduction)
• ✅ Deterministic representations (same input → same output → always)
• ✅ Semantic discrimination (negation, role reversal, synonyms)
• ✅ Associative binding/unbinding (key-value memory)
• ✅ Pure NumPy (no GPU, no PyTorch, no TensorFlow)

What HNM IS

Intended Uses

• Agent memory backbone: Long-term memory for LLM agents
• Semantic routing: Content-addressable dispatch
• Variable binding: Compositional reasoning substrate
• Edge deployment: Cognitive processing without GPU
• Distributed consensus: Deterministic state for multi-agent systems

How to Use

from hnm_v3 import HolographicNeuralMeshV3, HNMConfig

# Initialize
hnm = HolographicNeuralMeshV3(HNMConfig())

# Encode text
pattern, stats = hnm.forward("Machine learning is fascinating")
print(f"Latency: {stats['inference_time_ms']:.2f}ms, Sparsity: {1-stats['active_ratio']:.1%}")

# Semantic similarity
sim = hnm.similarity("I am happy", "I feel joyful")  # ~0.87
sim = hnm.similarity("dog bites man", "man bites dog")  # ~0.52 (role reversal detected)

# Memory storage and retrieval
hnm.encode_and_store("Deep learning uses neural networks")
hnm.encode_and_store("The stock market crashed today")
results = hnm.search("Tell me about neural networks", top_k=3)

# Associative binding
bound = hnm.bind("capital of France", "Paris")
recovered = hnm.unbind(bound, "capital of France")  # ≈ Paris vector

Benchmarks

Semantic Discrimination (7/7 Pass)

Scaling (Constant Time)

Resource Efficiency

Architecture

Input → Semantic Encoder → Holographic Projection → Interference Layers (×8) → Memory
            ↓                      ↓                        ↓                    ↓
      Word vectors +         Complex pattern          FFT + Phase mixing    Cleanup memory +
      Negation handling      Phase = semantics        99% sparsification    Iterative decoding

Key Components:

• Dual-channel encoding: Semantic (meaning) + Structural (order)
• Circular convolution binding: bind(key, value) → unbind(bound, key) ≈ value
• Cleanup memory: Iterative extraction from superposition
• Hierarchical storage: 16 slots with saturation monitoring

Limitations

1. Semantic priors are hand-crafted: ~40 word clusters cover common vocabulary but not open-domain language
2. Similarity is ordinal, not metric: Scores are internally consistent but not calibrated to SBERT
3. This is a substrate, not a system: Provides memory/binding, not language generation

Citation

@software{stone2024hnm,
  author = {Stone, Kent},
  title = {Holographic Neural Mesh: A Deterministic Sparse Semantic Substrate},
  year = {2024},
  publisher = {JARVIS Cognitive Systems},
  url = {https://huggingface.co/jarvis-cognitive/hnm-v3}
}

Lineage

HNM builds on established cognitive architecture research:

• Holographic Reduced Representations (Plate, 1995)
• Sparse Distributed Memory (Kanerva, 1988)
• Hyperdimensional Computing (Kanerva, 2009)
• Vector Symbolic Architectures (Kleyko et al., 2023)

Contact

Kent Stone - JARVIS Cognitive Systems