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# exo-core
Core traits and types for the EXO-AI cognitive substrate. Provides IIT
(Integrated Information Theory) consciousness measurement and Landauer
thermodynamics primitives that every other EXO crate builds upon.
## Features
- **SubstrateBackend trait** -- unified interface for pluggable compute
backends (classical, quantum, hybrid).
- **IIT Phi measurement** -- quantifies integrated information across
cognitive graph partitions.
- **Landauer free energy tracking** -- monitors thermodynamic cost of
irreversible bit erasure during inference.
- **Coherence routing** -- directs information flow to maximise substrate
coherence scores.
- **Plasticity engine (SONA EWC++)** -- continual learning with elastic
weight consolidation to prevent catastrophic forgetting.
- **Genomic integration** -- encodes and decodes cognitive parameters as
compact genomic sequences for evolution-based search.
## Quick Start
Add the dependency to your `Cargo.toml`:
```toml
[dependencies]
exo-core = "0.1"
```
Basic usage:
```rust
use exo_core::consciousness::{ConsciousnessSubstrate, IITConfig};
use exo_core::thermodynamics::CognitiveThermometer;
// Measure integrated information (Phi)
let substrate = ConsciousnessSubstrate::new(IITConfig::default());
substrate.add_pattern(pattern);
let phi = substrate.compute_phi();
// Track computational thermodynamics
let thermo = CognitiveThermometer::new(300.0); // Kelvin
let cost = thermo.landauer_cost_bits(1024);
println!("Landauer cost for 1024 bits: {:.6} kT", cost);
```
## Crate Layout
| Module | Purpose |
|---------------|----------------------------------------|
| `backend` | SubstrateBackend trait and helpers |
| `iit` | Phi computation and partition analysis |
| `thermo` | Landauer energy and entropy bookkeeping |
| `coherence` | Routing and coherence scoring |
| `plasticity` | SONA EWC++ continual-learning engine |
| `genomic` | Genome encoding / decoding utilities |
## Requirements
- Rust 1.78+
- No required system dependencies
## Links
- [GitHub](https://github.com/ruvnet/ruvector)
- [EXO-AI Documentation](https://github.com/ruvnet/ruvector/tree/main/examples/exo-ai-2025)
## License
MIT OR Apache-2.0