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bf25c65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | # Morpho-Logic Engine (MLE) — Adaptive Learning System
## Overview
The **Morpho-Logic Engine (MLE)** is a high-dimensional sparse distributed memory system with energy-based dynamics, optimized for CPU performance through bit-slicing SIMD operations. It learns continuously during inference without classical backpropagation, using purely local, energy-driven updates.
## Core Architecture
The system comprises five integrated modules that co-evolve during operation:
### 1. Memory — Adaptive Sparse Address Table
- **4096-bit binary vectors** with target sparsity ~5% (~200 active bits)
- **Dynamic creation**: new vectors spawn for recurrent or under-represented patterns
- **Fusion & specialization**: close vectors merge; context-dependent specializations branch off
- **Local reorganization**: semantic neighborhood coherence is improved iteratively
- **Controlled forgetting**: pruning of under-used entries prevents drift
### 2. Routing — Hamming Distance + Bit-Slicing SIMD
- Vectors packed into **64 × uint64** slices
- **Parallel Hamming distance** computation via bit-twiddling popcount
- **Inverted index** per slice for sub-linear candidate retrieval
- **Learned route cache**: frequently traversed query→neighbor mappings are memorized
### 3. Binding — Circular Convolution
- **Role-filler binding** via circular convolution in frequency domain (FFT)
- **Structure composition**: multiple role-filler pairs superposed into composite vectors
- **Robust unbinding**: recover fillers from bound representations
### 4. Energy Landscape — Learnable Coherence Function
- **Hamming energy**: local coherence via neighbor distances
- **Hebbian-like associations**: co-occurring vectors in low-energy states strengthen links
- **Anti-Hebbian for instability**: high-energy configurations weaken spurious associations
- **Adaptive biases**: per-bit biases shift based on experience
- **No global gradient**: all updates are purely local
### 5. Inference — Online Learning through Energy Minimization
- **Stochastic bit-flip descent** with simulated annealing temperature schedule
- **Metropolis-Hastings acceptance** for exploration/exploitation balance
- **Learning during inference**: associations, biases, and routes update at every iteration
- **Post-inference reinforcement**: stable low-energy trajectories are consolidated
## Key Capabilities
### Continuous Online Learning
The system learns while it reasons. Every inference pass updates:
- Vector co-activation weights
- Energy landscape associations
- Routing cache entries
- Memory structure (creation, fusion, specialization)
### Generalization through Composition
- **Binding/unbinding** enables compositional reasoning
- **Pattern abstraction** detects recurrent low-energy trajectories and compiles them into new memory units
- **Structure reuse**: existing sub-patterns are recycled in novel contexts
### Semantic Coherence
Local reorganization ensures vectors that are close in Hamming space correspond to semantically related concepts. Coherence score is continuously monitored.
### CPU-Optimized Performance
- All core operations use vectorized NumPy and JIT-compiled Numba kernels
- No dense matrix multiplications
- Bit-slicing reduces memory bandwidth by 64×
- Hamming distances computed via XOR + popcount
## Benchmark Results
```
Learning confirmed: ✓ Energy decreased with experience
Binding accuracy: 100% (10/10)
Semantic coherence: 0.996
Avg inference time: ~540 ms
Memory growth: controlled (auto-pruning)
Convergence rate: ~78%
```
## Usage
```python
from mle import MLESystem
import numpy as np
# Initialize
mle = MLESystem(
memory_capacity=2000,
online_learning=True,
temperature=0.5,
)
# Create a sparse input vector
vec = np.zeros(4096, dtype=np.uint8)
vec[np.random.choice(4096, size=200, replace=False)] = 1
# Process (inference + learning)
result = mle.process(vec)
print(f"Converged: {result.converged}")
print(f"Energy: {result.energy_trajectory[-1]:.1f}")
# Query neighbors
neighbors = mle.query(vec, k=5)
# Check system health
mle.print_summary()
```
## Directory Structure
```
mle/
├── __init__.py # Package exports
├── memory.py # Adaptive Sparse Address Table
├── routing.py # Hamming router with bit-slicing
├── binding.py # Circular convolution binder
├── energy.py # Learnable energy landscape
├── inference.py # Online learning inference engine
├── mle_system.py # Full system integration + metrics
└── tests.py # Comprehensive benchmark suite
```
## Design Principles
1. **Locality**: every update touches only a neighborhood, no global passes
2. **Sparsity**: 5% active bits → 95% of computation skipped implicitly
3. **Energy as teacher**: low energy = good, high energy = bad, no labels needed
4. **Memory is computation**: the memory table *is* the model; no separate weights
5. **Continuity**: training and inference are the same operation
## Future Directions
- Multi-resolution binding for hierarchical structures
- Cross-modal binding (vision + language in shared space)
- Energy landscape visualization and analysis
- Distributed memory shards for web-scale operation
- Integration with LLM token embeddings for hybrid reasoning
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