// std::ml::inference — sparse ternary inference primitives
// RFI-IRFOS Ternary Intelligence Stack
// use std::ml::inference;
// Ternary linear layer: sparse matrix-vector product
// W is a weight matrix (quantized to -1/0/+1), x is the input vector
// Zero-weighted connections are skipped entirely via @sparseskip
fn linear(W: trittensor<1 x 1>, x: trittensor<1 x 1>) -> trittensor<1 x 1> {
@sparseskip let out: trittensor<1 x 1> = matmul(W, x);
return out;
}
// Dense linear layer: no zero skipping (baseline comparison)
fn linear_dense(W: trittensor<1 x 1>, x: trittensor<1 x 1>) -> trittensor<1 x 1> {
let out: trittensor<1 x 1> = matmul(W, x);
return out;
}
// Ternary attention score: consensus of query and key projections
// Returns hold (0) when query and key are orthogonal — naturally sparse
fn attend(q: trit, k: trit) -> trit {
return consensus(q, k);
}
// Accumulate evidence: reduce a tensor to a single trit decision
// Counts truth vs conflict, returns whichever wins; ties → hold
fn decide(evidence: trittensor<1 x 1>) -> trit {
let s: trit = sparsity(evidence);
return s;
}