✅ HNM Is	❌ HNM Is Not
Semantic memory substrate	Language model
Symbolic binding engine	Next-token predictor
Deterministic cognitive layer	Embedding model replacement
Associative recall system	Foundation model

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

python

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)

Test	Pair	Score	Target	Status
Negation	"alive" / "not alive"	0.48	< 0.50	✅
Role Reversal	"dog bites man" / "man bites dog"	0.52	< 0.70	✅
Paraphrase	"happy" / "joyful"	0.87	> 0.70	✅
Unrelated	"neural networks" / "fishing"	0.01	< 0.30	✅

Scaling (Constant Time)

Corpus Size	TF-IDF	BM25	HNM
20 docs	0.03ms	0.04ms	1.78ms
2,000 docs	2.45ms	3.60ms	1.62ms
100× growth	78× slower	98× slower	0.9× slower

Resource Efficiency

Metric	Value
Sparsity	99%
FLOPS reduction	102×
Inference latency	~3.5ms
GPU required	No

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

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

Citation

bibtex

@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

KentStone/Holographic_Neural_Mesh

Holographic Neural Mesh (HNM) v3.0

Model Description

What HNM IS

Intended Uses

How to Use

Benchmarks

Semantic Discrimination (7/7 Pass)

Scaling (Constant Time)

Resource Efficiency

Architecture

Limitations

Citation

Lineage

Contact

KentStone/
Holographic_Neural_Mesh