feat(sparse-inference): TernarySparseFfn -- dual sparsity via ternary weights

crates/ruvector-graph-transformer-wasm/pkg/package.json

@@ -5,7 +5,7 @@
     "Ruvector Team"
   ],
   "description": "WASM bindings for ruvector-graph-transformer: proof-gated graph attention in the browser",
-  "version": "2.2.0",
+  "version": "2.2.2",
   "license": "MIT",
   "repository": {
     "type": "git",
@@ -28,4 +28,4 @@
     "verified",
     "webassembly"
   ]
-}
+}

crates/ruvector-sparse-inference/src/lib.rs

@@ -77,7 +77,7 @@ pub use precision::{
     Quantizer5Bit, Quantizer7Bit,
 };
 pub use predictor::{LowRankPredictor, Predictor};
-pub use sparse::{FeedForward, SparseFfn};
+pub use sparse::{FeedForward, SparseFfn, TernarySparseFfn};
 
 /// Sparse inference engine that coordinates prediction and computation
 pub struct SparseInferenceEngine {
@@ -89,7 +89,7 @@ pub struct SparseInferenceEngine {
 impl SparseInferenceEngine {
     /// Create a new sparse inference engine with sparsity
     ///
-    /// The sparsity_ratio determines what fraction of neurons are kept active (0.0-1.0)
+    /// The sparsity_ratio determines what fraction of neurons are kepped active (0.0-1.0)
     /// e.g., sparsity_ratio=0.3 means 30% of neurons are active (70% sparsity)
     pub fn new_sparse(input_dim: usize, hidden_dim: usize, sparsity_ratio: f32) -> Result<Self> {
         // Use top-K selection based on sparsity ratio for reliable activation

crates/ruvector-sparse-inference/src/sparse/mod.rs

@@ -3,9 +3,11 @@
 //! This module provides sparse implementations of neural network layers.
 
 mod ffn;
+mod ternary_ffn;
 
 pub use crate::config::ActivationType;
 pub use ffn::SparseFfn;
+pub use ternary_ffn::TernarySparseFfn;
 
 /// Trait for feed-forward network layers.
 pub trait FeedForward: Send + Sync {