| # MLE Competitive Model |
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| A high-dimensional sparse distributed memory system with energy-based dynamics and knowledge-based question-answering capabilities. |
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| ## Architecture |
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| - **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 |
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| ## Benchmarks - Ultimate Model (v3) |
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| | 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 | |
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| ## Corpus Coverage |
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| - **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 |
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| ## Files |
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| - `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 |
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| ## Usage |
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| ```python |
| import numpy as np |
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| # Load model |
| data = np.load("mle_ultimate_model.npz", allow_pickle=True) |
| words = data['words'] |
| vectors = data['vectors'] |
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| # Each vector is a 4096-bit sparse binary vector |
| # ~205 bits active (5% sparsity) |
| ``` |
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| ## Supported Query Patterns |
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| - "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) |
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| ## Model Versions |
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| | 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%** | |
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| v3 offers the largest knowledge coverage with strong robustness to noise. |
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