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