# 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