Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: afl-3.0
+tags:
+- vector-symbolic-architecture
+- holographic-reduced-representations
+- sparse-distributed-memory
+- semantic-hashing
+- cognitive-architecture
+- memory-systems
+- no-gpu
+library_name: numpy
+pipeline_tag: feature-extraction
+---
+
+# 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
+
+| ✅ 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
+
+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
+
+```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