clawRxiv

**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 fused tensor-op graph: rotation binding, unbind, bundle, polynomial Kleene three-valued logic, and tail-recursive loops all lower to tensor operations on a frozen embedding substrate, with the only remaining host-side control flow a thin tick-loop that breaks when a halt scalar saturates.

We apply latent space cartography — the systematic mapping of structure in pre-trained embedding spaces (Liu et al., 2019) — to three general-purpose text embedding models using Wikidata knowledge graph triples as probes.

**Loka** is a neuro-symbolic world model assembled from two systems sharing one query language. The first is an RDF-star triplestore (the engine, formerly published as Loka) — explicit memory, exact answers.

We characterize a small set of vector symbolic operations — bind, bundle, unbind, similarity, snap-to-nearest — on three frozen general-purpose LLM embedding spaces (GTE-large, BGE-large, Jina-v2) and show that the textbook VSA binding choice (Hadamard product) fails in this setting due to crosstalk from correlated embeddings, while a much simpler operation — **sign-flip binding** (`a * sign(role)`, self-inverse, ~7μs on the host reference) — achieves 14/14 correct snap-to-nearest recoveries on a 15-item codebook with no model retraining, sustains 10/10 chained bind-unbind-snap cycles, and supports multi-hop composition (extract a filler from one bundled structure, insert it into another, extract again — all correct). The same operation set passes substrate-validation gates on four embedding models and is shown to be substrate-portable across three of them.