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# exo-manifold
Continuous embedding space with SIREN networks for smooth manifold
deformation in cognitive AI. Provides the geometric foundation that
lets EXO-AI substrates represent and transform concepts as points on
learned manifolds.
## Features
- **SIREN coordinate network** -- uses sinusoidal representation
networks (SIREN) to learn implicit neural representations of
continuous coordinate spaces with high-frequency detail.
- **Manifold deformation** -- smoothly warps the embedding manifold to
adapt cognitive geometry in response to new information, preserving
local neighbourhood structure.
- **Transfer prior store with domain-pair indexing** -- caches learned
deformation priors indexed by (source, target) domain pairs so that
cross-domain transfers start from an informed initialisation.
## Quick Start
Add the dependency to your `Cargo.toml`:
```toml
[dependencies]
exo-manifold = "0.1"
```
Basic usage:
```rust
use exo_manifold::ManifoldEngine;
use exo_core::{ManifoldConfig, Pattern};
use burn::backend::NdArray;
// Create engine with default SIREN parameters
let config = ManifoldConfig::default();
let device = Default::default();
let mut engine = ManifoldEngine::<NdArray>::new(config, device);
// Deform manifold with a high-salience pattern
let pattern = Pattern { /* ... */ };
engine.deform(pattern, 0.9)?;
// Retrieve similar patterns via gradient descent
let query = vec![/* embedding */];
let results = engine.retrieve(&query, 10)?;
// Strategic forgetting of low-salience regions
engine.forget(0.5, 0.1)?;
```
## Crate Layout
| Module | Purpose |
|-------------|----------------------------------------------|
| `network` | SIREN network definition and forward pass |
| `retrieval` | Gradient descent retrieval algorithm |
| `deform` | Manifold deformation and curvature regulation |
| `forgetting`| Gaussian smoothing and weight pruning |
| `transfer` | Prior store with domain-pair indexing |
## Requirements
- Rust 1.78+
- Depends on `exo-core`, `burn`, `burn-ndarray`
## 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