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MLE — Morpho-Logic Engine
===========================
A novel reasoning AI architecture based on:
- 4096-bit binary hyperdimensional vectors
- Sparse distributed memory with Hamming distance proximity
- Circular convolution binding for semantic composition
- Energy-based dynamics for gradient-free reasoning
- SIMD-optimized CPU computation (bit-slicing + popcount)
Modules:
memory — Sparse Address Table with LSH indexing
routing — Recursive JIT Routing (beam search)
binding — Semantic binding via circular convolution & XOR
energy — Energy functions and relaxation dynamics
inference — Full reasoning engine
Quick start:
from mle import MorphoLogicEngine
engine = MorphoLogicEngine()
engine.add_concept("cat")
engine.add_relation("cat", "is_a", "animal")
result = engine.reason("cat")
"""
__version__ = "0.1.0"
__author__ = "MLE Team"
from .inference.reasoning_engine import ReasoningEngine as MorphoLogicEngine
from .memory import SparseAddressTable, HammingLSH
from .routing import RecursiveJITRouter, RoutingResult
from .binding import HRRBinding, BinaryBinding, BindingEngine
from .energy import EnergyFunction, RelaxationDynamics, HopfieldDynamics, EnergyModel
__all__ = [
'MorphoLogicEngine',
'SparseAddressTable', 'HammingLSH',
'RecursiveJITRouter', 'RoutingResult',
'HRRBinding', 'BinaryBinding', 'BindingEngine',
'EnergyFunction', 'RelaxationDynamics', 'HopfieldDynamics', 'EnergyModel',
]
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