wifi-densepose/vendor/ruvector/examples/exo-ai-2025/research/RUST_LIBRARIES.md

EXO-AI 2025: Rust Libraries & Crates Catalog

SPARC Research Phase: Implementation Building Blocks

This document catalogs Rust crates and libraries applicable to the EXO-AI cognitive substrate architecture.

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1. Tensor & Neural Network Frameworks

Primary Frameworks

Crate	Description	WASM	no_std	Use Case
burn	Next-gen DL framework with backend flexibility	✅	✅	Core tensor operations, model training
candle	HuggingFace minimalist ML framework	✅	❌	Transformer inference, production models
ndarray	N-dimensional arrays	❌	❌	General numerical computing
burn-candle	Burn backend using Candle	✅	❌	Unified interface over Candle
burn-ndarray	Burn backend using ndarray	❌	✅	CPU-only, embedded targets

Key Characteristics

Burn Framework:

// Burn's backend flexibility enables future hardware abstraction
use burn::backend::Wgpu;  // GPU via WebGPU
use burn::backend::NdArray;  // CPU via ndarray
use burn::backend::Candle;  // HuggingFace models

// Example: Backend-agnostic tensor operation
fn matmul<B: Backend>(a: Tensor<B, 2>, b: Tensor<B, 2>) -> Tensor<B, 2> {
    a.matmul(b)
}

Candle Strengths:

• Transformer-specific optimizations
• ONNX model loading
• Quantization support (INT8, BF16)
• ~429KB WASM binary for BERT-style models

Tensor Train Decomposition

Crate/Paper	Description	Status
Functional TT Library (Springer 2024)	Function-Train decomposition in Rust	Research

Note: This appears to be the only Rust-specific Tensor Train implementation, focused on PDEs rather than neural network compression. Opportunity exists for TT decomposition crate targeting learned manifold storage.

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2. Graph & Hypergraph Libraries

Core Graph Libraries

Crate	Description	Features	Use Case
petgraph	Primary Rust graph library	Graph/StableGraph/GraphMap, algorithms	Base graph operations
simplicial_topology	Simplicial complexes	Random generation (Linial-Meshulam), upward/downward closure	TDA primitives

petgraph Capabilities

use petgraph::Graph;
use petgraph::algo::{toposort, kosaraju_scc, tarjan_scc};

// Topological sort for dependency ordering
let sorted = toposort(&graph, None)?;

// Strongly connected components for hyperedge detection
let sccs = kosaraju_scc(&graph);

Simplicial Complex Operations

[dependencies]
simplicial_topology = { version = "0.1.1", features = ["sc_plot"] }

Supported Models:

• Linial-Meshulam (random hypergraphs)
• Lower/Upper closure
• Pure simplicial complexes

Gap Analysis

No dedicated Rust hypergraph crate exists. Current approach:

1. Use petgraph for base graph operations
2. Extend with simplicial_topology for TDA
3. Implement hyperedge layer consuming ruvector-graph

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3. Topological Data Analysis

Persistent Homology

Crate	Description	Features
tda	TDA for neuroscience	Persistence diagrams, Mapper algorithm
teia	Persistent homology library	Column reduction, persistence pairing
annembed	UMAP-style dimension reduction	Links to Julia Ripserer.jl for TDA

tda Crate Structure

use tda::simplicial_complex::SimplicialComplex;
use tda::persistence::PersistenceDiagram;
use tda::mapper::Mapper;

// Compute persistent homology
let complex = SimplicialComplex::from_point_cloud(&points, epsilon);
let diagram = complex.persistence_diagram();

teia CLI

# Compute homology generators
teia homology complex.json

# Compute persistent homology
teia persistence complex.json

Planned Features (teia):

• Persistent cohomology
• Lower-star complex
• Vietoris-Rips complex

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4. WASM & NAPI-RS Integration

WASM Ecosystem

Crate	Description	Use Case
wasm-bindgen	JS/Rust interop	Browser deployment
wasm-bindgen-futures	Async WASM	Async vector operations
web-sys	Web APIs	Worker threads, WebGPU
js-sys	JS types	ArrayBuffer interop

NAPI-RS for Node.js

Crate	Description	Use Case
napi	Node.js bindings	Server-side deployment
napi-derive	Macro support	Ergonomic API generation

Integration Pattern (ruvector style)

// NAPI-RS binding example
#[napi]
pub struct VectorIndex {
    inner: Arc<RwLock<HnswIndex>>,
}

#[napi]
impl VectorIndex {
    #[napi(constructor)]
    pub fn new(dimensions: u32) -> Result<Self> { ... }

    #[napi]
    pub async fn search(&self, query: Float32Array, k: u32) -> Result<SearchResults> { ... }
}

WASM Neural Network Inference

Tool	Description	Size
WasmEdge WASI-NN	TensorFlow/ONNX in WASM	Container: ~4MB
Tract	Native ONNX inference engine	Binary: ~500KB
EdgeBERT	Custom BERT inference	~429KB WASM + 30MB model

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5. Post-Quantum Cryptography

Primary Libraries

Crate	Description	Algorithms
pqcrypto	Post-quantum crypto	Multiple NIST candidates
liboqs-rust	OQS bindings	Full liboqs suite
kyberlib	CRYSTALS-Kyber	ML-KEM (FIPS 203)

NIST Standardized Algorithms

// Kyber example (key encapsulation)
use kyberlib::{keypair, encapsulate, decapsulate};

let (public_key, secret_key) = keypair()?;
let (ciphertext, shared_secret_a) = encapsulate(&public_key)?;
let shared_secret_b = decapsulate(&ciphertext, &secret_key)?;
assert_eq!(shared_secret_a, shared_secret_b);

Algorithm Support

• ML-KEM (Kyber): Key encapsulation
• ML-DSA (Dilithium): Digital signatures
• FALCON: Alternative signatures
• SPHINCS+: Hash-based signatures

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6. Distributed Systems & Consensus

Consensus Primitives

Crate	Description	Use Case
ruvector-raft	Raft consensus	Leader election, log replication
ruvector-cluster	Cluster management	Node discovery, sharding
ruvector-replication	Data replication	Multi-region sync

CRDT Candidates

Crate	Description	Status
crdts	CRDT implementations	Production-ready
automerge	JSON CRDT	Collaborative editing

ruvector Integration

// Existing ruvector-raft capabilities
use ruvector_raft::{RaftNode, RaftConfig};
use ruvector_cluster::{ClusterManager, NodeDiscovery};

let config = RaftConfig::default()
    .with_election_timeout(Duration::from_millis(150))
    .with_heartbeat_interval(Duration::from_millis(50));

let node = RaftNode::new(config, storage)?;

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7. Performance & SIMD

SIMD Libraries

Crate	Description	Use Case
simsimd	SIMD similarity functions	Distance metrics
packed_simd_2	Portable SIMD	General vectorization
wide	Wide SIMD types	AVX-512 operations

ruvector Usage

// simsimd for distance calculations (already in ruvector-core)
use simsimd::{cosine, euclidean, dot};

let similarity = cosine(&vec_a, &vec_b);
let distance = euclidean(&vec_a, &vec_b);

Parallelism

Crate	Description	Use Case
rayon	Data parallelism	Parallel iterators
crossbeam	Concurrency primitives	Lock-free structures
tokio	Async runtime	Async I/O, networking

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8. Serialization & Storage

Serialization

Crate	Description	Speed	Size
rkyv	Zero-copy deserialization	Fastest	Moderate
bincode	Binary serialization	Fast	Small
serde	Serialization framework	Varies	Varies

Storage Backends

Crate	Description	Use Case
redb	Embedded ACID database	Persistent storage
memmap2	Memory mapping	Large file access
hnsw_rs	HNSW index	Vector similarity

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9. Emerging Research Libraries

Neuromorphic Simulation

Status	Description	Gap
⚠️ Limited	No mature Rust SNN library	Opportunity

Current Options:

• Bind to C++ Brian2/NEST via FFI
• Port key algorithms from Python implementations
• Build minimal spike encoding layer

Photonic Simulation

Status	Description	Gap
⚠️ None	No Rust photonic neural network library	Major gap

Approach: Abstract optical matrix-multiply as backend trait

Memristor Simulation

Status	Description	Gap
⚠️ None	No Rust memristor crossbar simulation	Research opportunity

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10. Recommended Stack for EXO-AI

Core Foundation (ruvector SDK)

[dependencies]
ruvector-core = "0.1.16"
ruvector-graph = "0.1.16"
ruvector-gnn = "0.1.16"
ruvector-raft = "0.1.16"
ruvector-cluster = "0.1.16"

ML/Tensor Operations

burn = { version = "0.14", features = ["wgpu", "ndarray"] }
candle-core = "0.6"
ndarray = { version = "0.16", features = ["serde"] }

TDA/Topology

petgraph = "0.6"
simplicial_topology = "0.1"
teia = "0.1"
tda = "0.1"

Post-Quantum Security

pqcrypto = "0.18"
kyberlib = "0.0.6"

WASM/NAPI

wasm-bindgen = "0.2"
napi = { version = "2.16", features = ["napi9", "async", "tokio_rt"] }
napi-derive = "2.16"

Distribution

tokio = { version = "1.41", features = ["full"] }
rayon = "1.10"
crossbeam = "0.8"

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Library Maturity Assessment

Category	Maturity	Notes
Tensors/ML	🟢 High	Burn, Candle production-ready
Graphs	🟢 High	petgraph is mature
Hypergraphs	🟡 Medium	Need to build on simplicial_topology
TDA	🟡 Medium	tda/teia usable, feature-incomplete
PQ Crypto	🟢 High	Multiple options, NIST standardized
WASM	🟢 High	wasm-bindgen ecosystem mature
NAPI-RS	🟢 High	ruvector already uses successfully
Neuromorphic	🔴 Low	Major gap, build or bind
Photonic	🔴 Low	No existing libraries
Memristor	🔴 Low	Research prototype needed