Upload README.md

README.md

@@ -4,28 +4,50 @@ A high-dimensional sparse distributed memory system with energy-based dynamics a
 
 ## Architecture
 
-- **Vector size**: 4096 bits (sparse, ~5% active)
-- **Memory**: Sparse Address Table with online learning
-- **Inference**: Energy minimization via simulated annealing
-- **Knowledge**: 283 facts, 334 words, 53 sequences
-
-## Benchmarks
-
-| Task | Accuracy |
-|------|----------|
-| Question Answering | 92.9% |
-| Analogy Solving | 100.0% |
-| Sequence Completion | 93.3% |
-| Word Retrieval | 100.0% |
-| Generalization | 100.0% |
-| **Overall** | **97.2%** |
+- **Vector size**: 4096 bits (sparse, ~5% active = 205 bits)
+- **Memory**: Sparse Address Table with deterministic hash encoding
+- **Knowledge base**: 1848 facts, 1106 words, 53 sequences
+- **Encoding**: MD5-based deterministic sparse vectors
+- **Inference**: Symbolic reasoning with Hamming similarity
+
+## Benchmarks - Ultimate Model (v3)
+
+| Task | Accuracy | Details |
+|------|----------|---------|
+| **Question Answering** | **62.8%** | 49/78 correct |
+| **Analogy Solving** | **67.9%** | 19/28 correct |
+| **Sequence Completion** | **93.3%** | 14/15 correct |
+| **Word Retrieval (noise 15%)** | **100%** | 100/100 correct |
+| **Generalization** | **100%** | 20/20 correct |
+| **Robustness (noise 50%)** | **100%** | Up to 50% bit corruption |
+| **Overall** | **84.8%** | Weighted average |
+
+## Corpus Coverage
+
+- **Animals**: 185 animals across 5 categories
+- **Plants**: 175 plants across 6 categories
+- **Locations**: 150+ cities across 15 countries and 5 continents
+- **Vehicles**: 10 vehicles with parts and actions
+- **Professions**: 20 professions with workplace associations
+- **Food**: 55 food and drink items
+- **Body**: 36 body parts with functions
+- **Colors**: 29 colors with associations
+- **Emotions**: 20 emotions with opposites
+- **Time**: Full temporal sequences (days, months, seasons, ordinals)
+- **Tools**: 20 tools with functions
+- **Music**: 17 instruments and 12 genres
+- **Sports**: 22 sports
+- **Weather**: 14 weather types
+- **Clothing**: 20 clothing items
+- **Technology**: 19 devices
+- **Celestial**: 20 celestial bodies
 
 ## Files
 
-- `mle_best_facts.json`: Knowledge base facts
-- `mle_best_model.npz`: Word vectors and embeddings
-- `mle_best_results.json`: Benchmark results
-- `mle_best_config.json`: Model configuration
+- `mle_ultimate_model.npz` - Word vectors (1106 × 4096 binary)
+- `mle_ultimate_facts.json` - Complete knowledge base (1848 facts)
+- `mle_ultimate_results.json` - Benchmark results
+- `mle_ultimate_config.json` - Model configuration
 
 ## Usage
 
@@ -33,7 +55,32 @@ A high-dimensional sparse distributed memory system with energy-based dynamics a
 import numpy as np
 
 # Load model
-data = np.load("mle_best_model.npz", allow_pickle=True)
+data = np.load("mle_ultimate_model.npz", allow_pickle=True)
 words = data['words']
 vectors = data['vectors']
+
+# Each vector is a 4096-bit sparse binary vector
+# ~205 bits active (5% sparsity)
 ```
+
+## Supported Query Patterns
+
+- "what is X" → category (is_a)
+- "what has X" → possessor (has)
+- "what can X" → agent (can)
+- "where is X" → location (in/lives_in)
+- "what color is X" → color (is/has_color)
+- "what is opposite of X" → antonym (opposite)
+- "what is before X" → predecessor (before)
+- "what is X made of" → material (made_of)
+- "what is X used for" → function (used_for)
+
+## Model Versions
+
+| Version | Facts | Words | Overall |
+|---------|-------|-------|---------|
+| v1 (mle_best) | 283 | 334 | **97.2%** |
+| v2 (mle_advanced) | 933 | 705 | **68.2%** |
+| v3 (mle_ultimate) | 1848 | 1106 | **84.8%** |
+
+v3 offers the largest knowledge coverage with strong robustness to noise.