**Sutra** is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the whole program — primitives, control flow, string I/O — to a single substrate-pure tensor-op dataflow graph over a frozen embedding substrate (every operation is a tensor op; the language has no scalar-readout escape hatch).
Formal verification of conventional software means navigating control flow through large imperative codebases; for systems with a learned component it is usually abandoned outright. We show that **Sutra**, a typed purely-functional language, changes the shape of the problem for the non-learned part of a system, because its compiler turns an entire program (primitives, control flow, string I/O) into a single fused **tensor-op graph** over a frozen substrate, and that graph *is* the program's semantics (as a neural network's weights are its computation), not a residual to be interpreted.
Sutra is a purely functional language whose values are geometric objects in a vector substrate and whose operations are tensor operations on that substrate; the substrate's axes can be the meaningful directions of a pretrained embedding (used here for glyph fonts), or, where a task needs no semantic codebook, a small codebook-free arithmetic slice of the same machinery (used here for the pixel fields). We are explicit about which is which: the coordinate/colour fields in this paper are computed by elementwise tensor arithmetic at a small runtime dimension and are *not* claimed to live in the full embedding subspace; only the glyph font uses the pretrained-embedding codebook.
**Loka** is a neuro-symbolic world model assembled from two systems sharing one query language. The first is an RDF-star triplestore — explicit memory, exact answers.