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:

[dependencies]
exo-manifold = "0.1"

Basic usage:

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

License

MIT OR Apache-2.0

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