Sutra: Compiling a Vector Symbolic Architecture to a Tensor-Op Recurrent Neural Network via Beta Reduction

Abstract

Sutra is a typed, purely functional programming language; a compiled Sutra program is a PyTorch neural network. Every primitive — rotation binding, unbind, bundle, similarity, soft-halt RNN cells, polynomial Kleene three-valued logic — compiles to a tensor op, and the compiler beta-reduces the whole program (control flow included) to a fused tensor-op graph whose substrate-resident computation is straight-line dataflow: no in-graph branches inside any operation, no string-keyed lookup at runtime, and no Python control flow inside the body of a loop cell — the only remaining host-side control flow is a thin tick-loop that breaks when a substrate-computed halt scalar saturates (§3.4). The contribution is the construction that makes this isomorphism land: a symbolic source language whose compiled forward pass is a substrate-pure neural network, autograd-compatible by construction, executable wherever PyTorch executes. We validate the language across four frozen embedding substrates spanning two modalities — three text encoders (nomic-embed-text, all-minilm, mxbai-embed-large) and one protein language model (ESM-2) — and observe the same rotation-vs-Hadamard separation across modalities: rotation binding decodes at 100% accuracy through bundle width k=8 on every substrate, where Hadamard binding has already collapsed (e.g. 2.5% on mxbai-embed-large, 28.7% on ESM-2), with single-cycle bind/unbind exactly reversible (round-trip ≈ 1.5×10⁻¹⁵). The program-network identity is end-to-end testable through PyTorch autograd: a symbolic if-then program of fuzzy rules over twenty classes (animal, vehicle, food, color, clothing, weather, emotion, tool, instrument, profession, body-part, plant, furniture, building, country, sport, drink, metal, shape, fabric; 992 words total, K=20 rule tree nineteen ANDs deep) trains from chance accuracy (4%) to 95% in 300 epochs, with nonzero gradient at every prototype and no modification to the symbolic source — gradient descent moves the embeddings the rules evaluate against, not the rule graph itself.

1. Introduction

The discovery that general-purpose language model embeddings encode relational structure as vector arithmetic — king − man + woman ≈ queen, formalized through TransE, RotatE, and the broader knowledge-graph embedding literature — established that there is genuine algebraic content in the geometry of pre-trained models. Given that algebraic structure exists, two questions follow:

1. Which operations on these embeddings are reliable enough to be used as primitives of a compositional algebra over the embedding space, rather than as one-off lexical facts?
2. What is the correct binding operation to compose those primitives into structured representations — i.e. how do we build a working vector-symbolic architecture (VSA) on top of substrates the standard VSA literature was not designed for?

This paper answers both questions in the form of a working programming language, Sutra, whose primitives are exactly these consolidated operations. The naming: Sutra is the Sanskrit sūtra — thread, rule, aphorism — the term for Pāṇini's foundational Sanskrit grammar.

1.1 Contributions

The four core technical contributions of this paper are:

1. Polynomial fuzzy logic via Lagrange interpolation of Kleene's three-valued truth tables. The truth axis encodes T = +1, U = 0, F = −1. On the discrete {−1, 0, +1} grid, the Kleene connectives are AND = min, OR = max, NOT = −·. The min/max forms (the standard Gödel t-norm/t-conorm choice; Hájek 1998) are non-differentiable at the diagonal a = b, which breaks gradient flow when connectives compose with the tensor-op graph (van Krieken, Acar & van Harmelen 2022 survey the issue across t-norm-derived neural-symbolic operators). Sutra resolves this by Lagrange-interpolating each connective as a polynomial that is exact on the 3×3 Kleene grid and C^∞ elsewhere:

   • AND(a, b) = (a + b + ab − a² − b² + a²b²) / 2
   • OR(a, b) = (a + b − ab + a² + b² − a²b²) / 2
   • NOT(a) = −a
   • XOR(a, b) = −ab, XNOR(a, b) = ab

   {AND, OR, NOT} is functionally complete for the Kleene fragment; XOR/XNOR collapse to a single multiplicative term because their interpolant is zero whenever either input is U and bilinear in the {−1, +1} corners. Every Kleene-valid connective is therefore a polynomial tensor-op-graph fragment — gradient-compatible, branchless, and exact on the discrete-logic regime. A symbolic if-then rule built from these gates is one fused subgraph that PyTorch autograd backprops through end-to-end (§3.6).

2. Beta reduction to tensor normal form. The compiler inlines stdlib operator definitions, beta-reduces through bound names, then runs an algebraic-simplification pass over the residual. What's left is a fused tensor-op graph (matmul / element-wise / nonlinear) with no named bindings or function calls. Three concrete moves go beyond standard inlining + constant folding: conditionals lower to soft-mux polynomials ((1+cond)/2·a + (1−cond)/2·b) so the compiled artifact has no if opcodes; Haar-orthogonal binding rotations R_role are materialized at compile time so runtime bind is one matmul against a constant matrix; canonical synthetic axes are assigned compile-time so every primitive-type read/write is a known index, not a hashtable lookup. §4.3 traces this lowering stage-by-stage on a concrete program; Figure 1 shows the compilation pipeline.

3. Tail recursion as the loop primitive. Loops are tail-recursive function declarations (do_while, while_loop, iterative_loop, foreach_loop) whose body's return NAME(args) becomes the recurrent step. Each loop compiles to a soft-halt RNN cell with substrate-pure halt detection (heaviside → cumulative monotone halt → soft-mux state freeze). The body of every loop tick is one straight-line tensor pipeline with no in-graph branches; a thin Python while True: … break driver wraps the body and terminates when the halt scalar saturates (§3.4). The state vector is fixed-width across iterations — O(1) state, O(N) compute, O(N) gradient tape during training, where N is iterations actually executed.

4. Synthetic-dimension rotation binding as an angular hash map. The compiler reserves a synthetic block of canonical dimensions and uses Haar-orthogonal rotations seeded from the role's content hash to bind keys to slots. To the authors' knowledge this is the first use of a high-dimensional rotation pattern as the substrate for a functional hash-map primitive.

These four primitives integrate into a single working compiler that lowers .su source to a self-contained PyTorch module on CPU or CUDA.

A fifth result is engineering, not theoretical: end-to-end string I/O through the substrate via a compile-time codebook + nearest_string decode (§3.5). The frozen-LLM embedding gives a deterministic string-to-vector map that the compiler bakes into a .sdb codebook at build time; the inverse decode runs at the program output boundary. Existing HDC libraries (TorchHD and similar) require the user to maintain a string-to-vector dictionary and codebook tensor by hand. To the authors' knowledge Sutra is the only HDC implementation that ships this as a built-in compiler concern.

1.2 The substrate is the architecture target

A Sutra program is compiled for an embedding-space architecture, the way a C program is compiled for x86 and a CUDA kernel for an NVIDIA SM. The embedding model fixes dimensionality, the geometry of the semantic block, and the meaning of every basis-vector lookup; swap the model and the same source recompiles to a different .sdb codebook against a different geometry. The substrate need not be an LLM — it can be any network producing a dense vector representation, including the hidden state of a trained model. §3.2's ESM-2 protein-LM row demonstrates this substrate-agnostically.

2. Related Work

2.1 Vector Symbolic Architectures

VSA is a family of algebraic frameworks for computing with high- dimensional vectors (Kanerva 2009; Plate 1995; Gayler 2003). The standard VSA development assumes hypervectors drawn from a controlled random distribution designed for the algebra; bind is typically Hadamard product or circular convolution. Frozen LLM embedding spaces are not designed for VSA, and the textbook bind operations do not always transfer cleanly to them. Rotation binding (R_role @ filler for a role-seeded Haar-random orthogonal R_role) is the choice that worked across the substrates we tested, and is what Sutra uses today; §3.2 reports the per-substrate measurements supporting that choice.

The closest software peer in the VSA space is TorchHD (Heddes et al. 2023), a PyTorch library that exposes VSA primitives (bind, bundle, similarity) as tensor operations. Sutra and TorchHD differ on what the user writes and what the compiler does:

• TorchHD is a library. The user writes Python code that calls TorchHD primitives; control flow is host-side Python; there is no source-language layer above the primitives, no compile step, and no algebraic reduction across primitive calls. Each primitive call is a tensor op, but the program itself is a Python function with whatever control flow the user wrote.
• Sutra is a language with a compiler. The user writes .su source which the compiler beta-reduces to tensor normal form (§1.1-2): a single straight-line tensor-op graph with no Python control flow. Loops are tail-recursive function declarations that lower to soft-halt RNN cells; conditionals are differentiable fuzzy interpolations rather than Python if. Hash-map structure is implemented via synthetic-dimension rotation, not via a host-side dictionary.

This is not a "TorchHD is bad" claim; TorchHD is the right tool for using VSA primitives as a library in a Python program. Sutra is the construction that compiles a separate source language to the same primitive set with no host-side residue, which TorchHD is not designed to do.

A second axis where Sutra differs from existing HDC software is string I/O. TorchHD and similar libraries expose the algebra over user-supplied hypervectors; the user maintains a dict[str, hypervector] and an explicit codebook tensor by hand. Sutra's compile-time codebook (§3.5) closes that loop: every embedded string in .su source is embedded once at compile time via the configured frozen LLM, stored in the project's .sdb codebook, and decoded at the program output via nearest_string. The frozen-LLM embedding is load-bearing — random hypervectors yield a working VSA algebra with no I/O story.

A worked side-by-side of the same 3-field role-filler-record task in Sutra and TorchHD is in Appendix C; the structural differences (Sutra contains no Python, automatic string-to-vector mapping, implicit codebook construction, single fused tensor-op graph) are differences in artifact shape, not library speed.

2.2 Comparison to other neuro-symbolic languages

The closest neuro-symbolic-language peers — Scallop (Li et al. 2023, Datalog with provenance-semiring differentiability), DeepProbLog (Manhaeve et al. 2018, ProbLog with neural predicates), Logic Tensor Networks (Badreddine et al. 2022, first-order logic compiled to t-norm losses), and NeurASP (Yang et al. 2020, Answer Set Programming with neural predicates) — all share a two-stage perception-then-reasoning shape: a neural model extracts discrete symbols from raw input, and a symbolic program reasons over those symbols. Sutra's shape is different at this architectural level: the substrate is a continuous embedding space throughout, primitives operate on vectors end-to-end, and the whole program — including what would be the logic program in Scallop — compiles to a single fused tensor-op graph through beta reduction. There is no discrete symbolic stratum to extract into or reason over; differentiability is inherited from the tensor-op graph itself, not from a provenance annotation on a relational query. The two are good at different problem structures: Scallop and its peers when the problem is naturally relational and perception cleanly factors out; Sutra when computation is best expressed as algebra on vectors over a substrate the program reads strings into and decodes strings out of.

The closest HDC peer with compiler infrastructure is HDCC (Vergés et al. 2023), a description-file DSL targeting self-contained C for embedded classification — random/level hypervectors only, no general control flow, scoped to classification. TorchHD and OpenHD / HDTorch are libraries without a language-level loop primitive. To the authors' knowledge, no published HDC system combines (a) one fused tensor-op graph as compile target, (b) HDC primitives as the operations, (c) a frozen externally-trained vector embedding space as the substrate, and (d) tail-recursive loops compiled to soft-halt RNN cells with constant state-vector width in recursion depth. The combination is what distinguishes Sutra, not any one of those properties in isolation.

2.3 Differentiable Programming, AOT Compilation, and Knowledge

Compilation

The closest design ancestors are partial-evaluation systems that specialize programs at compile time (the Futamura projections), differentiable programming systems that treat programs as differentiable functions (JAX), AOT compilation of neural networks (TVM, XLA), and knowledge compilation in symbolic AI (Darwiche & Marquis 2002). Sutra differs from each: TVM/XLA start from a network, not toward one; JAX treats programs as differentiable but does not bake source literals into weights; partial evaluation specializes for compile-time-known values but does not target a neural-network-shaped artifact; knowledge compilation targets Boolean circuits, not continuous embedding spaces. Sutra's combination — fold source literals into the weight structure, compile control flow to RNN cells, run the whole program as one tensor-op graph over a continuous substrate — is the novel position.

3. Consolidation into Canonical Primitives

The central design move: hold the operation interface fixed and pick a binding implementation that works on dense externally-trained substrates. Standard VSA's Hadamard product fails here — elementwise multiplication of correlated real-valued vectors produces destructive crosstalk on bundled retrieval (§3.2 measures this directly). Rotation binding works: each role gets a Haar-random orthogonal R_role seeded by hash(role), and bind(role, filler) = R_role @ filler is invertible (unbind is the transpose) and well-conditioned. The compiler caches R_role per-role at module init so runtime bind is a single matmul against a precomputed matrix.

3.1 Notation

We work in a fixed-dimensional real vector space ℝᵈ where d is the substrate's embedding dimension (768 for nomic-embed-text, 384 for all-minilm, 1024 for mxbai-embed-large, 320 for ESM-2). Every Sutra value carries the extended layout [semantic | synthetic] — a d-dimensional semantic block holding the substrate embedding, concatenated with a small fixed-width synthetic block reserving canonical axes for primitive types (real, imag, truth, char, loop-done) and slot machinery (§3.3). Where notation does not distinguish, "vector" means "the full extended-layout tensor."

The seven primitive operations are:

The Lagrange gates are reproduced compactly here for reference:

AND(a, b)  =  (a + b + ab − a² − b² + a²b²) / 2
OR(a, b)   =  (a + b − ab + a² + b² − a²b²) / 2
NOT(a)     =  −a
XOR(a, b)  =  −ab
XNOR(a, b) =  ab

The soft-halt cell update (§3.4) is, in compact form,

   sₜ₊₁  =  R · sₜ                               (rotation step)
   hₜ    =  Heaviside( cond(sₜ) )                (per-tick halt signal)
   Hₜ    =  saturate_unit( Σₖ≤ₜ hₖ )             (cumulative monotone halt)
   ŝₜ₊₁  =  Hₜ · sₜ + (1 − Hₜ) · sₜ₊₁           (soft-mux freeze)

Every right-hand side is a tensor expression with no Python control flow. The compile-time primitives RotationFor and embed produce constants Rᵣ and basis vectors at compile time and are not part of the runtime tensor graph.

3.2 Capacity of rotation versus Hadamard binding across substrates

We measure decode accuracy as a function of bundle width k on real embeddings — not on random fillers — across four substrates spanning two modalities: three frozen LLM text encoders (nomic-embed-text, all-minilm, mxbai-embed-large) and one frozen protein language model (ESM-2 small, facebook/esm2_t6_8M_UR50D). The protein-LM substrate embeds an 84-sequence amino-acid vocabulary (canonical signal peptides, cell-penetrating peptides, antimicrobial peptides, classic affinity-tag motifs, and deterministic random k-mers); the LLM substrates each embed the same 84-word noun vocabulary (animals, foods, objects, places, abstract nouns) via Ollama. All embeddings are unit-normalized; nomic-embed-text and ESM-2 are additionally mean-centered. For each bundle width and each binding scheme we run 10 trials, each sampling k random (role, filler) pairs without replacement, forming the bundle, and decoding by unbind + argmax-cosine against the full codebook. The two binding schemes compared are rotation binding (R_role @ filler, role-seeded Haar-random orthogonal R_role) and Hadamard binding (elementwise product role .* filler, the textbook MAP-VSA choice).

Cross-substrate decode accuracy at representative bundle widths (per-substrate full k ∈ {2, 4, 8, 16, 24, 32, 48} sweeps in Appendix E):

ESM-2 (Lin et al., Science 2023) is a frozen protein language model trained on UniRef sequences with no exposure to natural language; its embedding space encodes amino-acid context, not word semantics. The same rotation-vs-Hadamard separation appears in this entirely different modality: rotation holds 100% accuracy through k=8 and degrades gracefully; Hadamard collapses fast.

Reversibility round-trip (rotation): mean ‖unbind(R, bind(R, x)) − x‖ = 1.5 × 10⁻¹⁵ across all four substrates — floating-point round-off, since Haar-random Q is orthogonal so QᵀQ = I.

The substrate-agnosticism claim is not a hedge: the same shape (rotation graceful-degradation, Hadamard fast-collapse) reproduces on a dense vector space from a protein language model that has never seen natural-language text. Sutra's rotation primitive is sensitive to dense high-dimensionality, not to whether the substrate was trained on words. Reproduction scripts: experiments/rotation_binding_capacity_llm.py (LLM substrates) and experiments/rotation_binding_capacity_bioinformatics.py (ESM-2); raw JSON committed alongside.

3.2.1 Noise accumulation across chained bind/unbind cycles

The §3.2 protocol measures one bind+bundle+unbind cycle. Nested records — a recovered filler becoming the role of a sub-record — add bundle noise per level. We measured this directly: chain lengths L ∈ {1, 2, 4, 8, ...}, 20 trials, bundle width 4. Raw accuracy holds at 100% through L=2 on every substrate and falls to chance (1/84) by L=8. The demonstrated regime is therefore single-cycle records, which matches the shape of the role_filler_record, knowledge_graph, and predicate-lookup demos. Pure rotation chains without per-step distractor bundling remain exact (round-trip 1.5×10⁻¹⁵ per cycle), so the noise mechanism here does not apply to the soft-halt loop cell of §3.4. Reproduction script: experiments/crosstalk_chain.py; full per-substrate L-sweep tables in Appendix A.

3.3 The extended-state-vector layout

Every value carries a fixed [semantic | synthetic] layout: the d-dimensional semantic block holds the substrate embedding for vector-shaped values, and a small synthetic block reserves canonical axes for primitive types (real, imag, truth, char) and a loop-completion flag, with the remaining axes paired into 2D Givens planes for variable slots. Default at d = 768 (nomic-embed-text): a 100-dim synthetic block accommodates the five canonical axes plus 47 disjoint slots. Rotation binding is block-diagonal across the split (Q_role is Haar-random in the semantic block, identity on the synthetic block), so the synthetic axes pass through bind/unbind unchanged — a fuzzy-truth scalar can coexist with a semantic vector inside the same value without bind smearing them. Full per-axis purpose table and slot allocator details in Appendix D.

3.4 First-class loops as RNN cells

Runtime data-dependent loops compile to self-halting RNN cells. Each tick: snapshot pre-step state, evaluate the halt condition on the substrate (truth-axis read → heaviside step → cumulative saturating sum), run the cell body, then a soft-mux blends pre-step and new-step state weighted by the halt accumulator. A Python while True: driver breaks the moment halt_cum saturates.

            state_in
               |
        +------+------+
        |             |
        v             v
    pre_state    cell body (pure tensor ops)
                      |
                      v
                 new_state, halt_signal
                      |
              halt_cum  ← saturating sum
                      |
                      v
              soft-mux freeze:
              state_out = (1 - halt_cum) · new_state
                        +     halt_cum  · pre_state

Once halt_cum saturates, the soft-mux output is pre_state — the loop has frozen. The Python driver checks halt_cum once per tick and breaks; this is the only host-side branch in the loop machinery. Inside the cell body, every operation is a substrate tensor op. There is no compile-time iteration cap — programs terminate when their halt condition fires, exactly the way any other programming language's while loop does. The halt-cum read is a boundary operation of the same shape as the codebook decode (§3.5).

Loop body vs loop driver. Tensor normal form applies to the body of each tick, not to the loop itself: standard PyTorch tracing handles a Python while-loop wrapping pure tensor ops, and autograd records each iteration as it executes — the mechanism §3.6 relies on for end-to-end backprop through the cell.

Constant memory in recursion depth. The state vector is fixed-width and shared across iterations, so a tail-recursive loop consumes O(1) memory in the state vector regardless of trip count: no per-step stack frame, no growing context. Compute is O(N) and the autograd tape during training is O(N) in iterations actually executed (standard PyTorch behavior, freed after backward). To the authors' knowledge no other HDC system or compiler exposes user-program-level recursion: HDCC is scoped to classification pipelines, TorchHD requires the user to write Python loops over hypervectors. The recurrent shape that emerges is the same one Siegelmann & Sontag (1992) showed can compute any Turing-machine-computable function with rational weights.

3.5 Embedded codebook store

Every embedded string in a Sutra program is embedded once at compile time and stored in a .sdb codebook that ships alongside the compiled module. The runtime decode _VSA.nearest_string(query) returns the nearest-string label for any query vector; the lookup runs at the program's output boundary, returning a host string the same way any compiled program returns a host value. Calling a well-engineered ANN library at this boundary is shape-equivalent to calling PyTorch for a matmul — neither is the kind of host-side control flow substrate purity forbids. Implementation details (RDF triple layout, HNSW graph parameters, .sdb file format, complexity analysis) are in Appendix B.

3.6 End-to-end differentiable training through Sutra operations

Because every Sutra primitive compiles to a differentiable tensor operation, the compiled graph supports standard PyTorch loss.backward() without modification. We verify this by training learnable parameters through a fuzzy-logic classifier built entirely from Sutra operations.

Setup. 992 words across twenty semantic categories (50 each, deduplicated; full list in Appendix F) are embedded via nomic-embed-text (768-d, frozen). Twenty learnable prototype vectors are initialized randomly. The classifier computes cosine similarity between input and each prototype and applies a Lagrange-interpolated fuzzy if-then rule:

rule_i = AND(sim(x, proto_i), AND_{j ≠ i} NOT(sim(x, proto_j)))

with the AND-of-NOTs left-folded across K−1 other classes (so the K=20 rule nests nineteen ANDs deep). Full-batch cross-entropy over the twenty rule scores drives Adam updates (lr=0.005) on the prototype embeddings.

Results. Random init: 4% accuracy (chance = 5%). Training reaches 95% by epoch 50 and holds through epoch 299, loss converging to 1.154. Gradient norms at all twenty prototypes are nonzero throughout (range 0.94–4.20), so backprop reaches every learnable parameter through similarity → fuzzy_not → nineteen nested fuzzy_and → cross-entropy.

Figure 2: The rule pipeline as a tensor-op graph. Drawn for K=3 to fit on the page; the K=20 graph used in the experiment has the same shape with twenty learnable prototypes and the AND-of-NOTs left-folded across nineteen NOT(sim) terms (so the rule for class i nests nineteen ANDs deep). Every box is a PyTorch tensor op; every edge carries either a vector (d-dimensional) or a scalar. There are no Python branches, no host-side dispatch, and no string-keyed lookup — backprop reaches every learnable parameter through the same compiled graph that runs at inference.

                         input  x ∈ ℝᵈ
                              │
            ┌─────────────────┼─────────────────┐
            │                 │                 │
            │   p₁ (learnable)│   p₂ (learnable)│   p₃ (learnable)
            │                 │                 │
            ▼                 ▼                 ▼
       cos(x, p₁)         cos(x, p₂)        cos(x, p₃)
            │                 │                 │
         sim₁ (∈ℝ)         sim₂ (∈ℝ)        sim₃ (∈ℝ)
            │                 │                 │
            │                 ▼                 ▼
            │             NOT (= −·)        NOT (= −·)
            │                 │                 │
            │              −sim₂             −sim₃
            │                 │                 │
            │                 └──── AND ────────┘
            │                          │
            │                     neg_others
            │                          │
            └────── AND  ──────────────┘     ← Lagrange polynomial:
                          │                    AND(a,b) = (a+b+ab
                          ▼                         −a²−b²+a²b²)/2
                       rule₁ (∈ℝ)
                          ⋮
        (rule₁, rule₂, rule₃)  ─────►  × temperature  ─────►  softmax
                                                                  │
                                                                  ▼
                                                       cross-entropy(label)
                                                                  │
                                                                  ▼
                                                                 loss

At K=20 the rule for class i is an AND of sim(x, proto_i) with a left-folded chain of nineteen NOT(sim) terms — a tensor pipeline that could naively saturate or vanish gradients somewhere along the chain. Empirically it doesn't: every prototype receives a nonzero gradient, accuracy reaches 95% on a vocabulary 70× larger than the K=3 setting (15 → 992 words), and the symbolic program text is unchanged across training. The remaining 5% gap is honest semantic overlap (e.g. salmon fits food and color); gradient norms remain bounded above zero throughout, so this is the optimizer plateauing under those overlaps, not gradient pathology. Standard torch.autograd suffices — no Sutra-specific autograd machinery — because the compiler emits only operations PyTorch already knows how to differentiate. Reproduction: experiments/differentiable_training.py + raw JSON.

4. The Sutra Compiler

The compiler is a five-stage pipeline:

1. Lex + parse — .su source → AST.
2. Inline + simplify — stdlib operator definitions inlined; an egglog-based simplifier folds equivalent expressions and runs common-subexpression elimination over the algebra.
3. Codegen — AST → Python source emitting PyTorch tensor ops. The emitted module includes the runtime class (_TorchVSA) as inline source so the artifact is self-contained.
4. Compile-time substrate population — embed_batch fetches embeddings for every string literal; populate_sutradb pushes the codebook into SutraDB; prewarm_rotation_cache precomputes role rotations.
5. Execute — emitted module loaded; chosen device (CUDA or CPU) initialized at module import; main() called; result returned.

The runtime class is emitted inline rather than imported because the emitted module is the substrate-pure tensor-op graph; the compile-time decisions (extended-state-vector dimensions, codebook contents, role rotations, SutraDB path, optional torch.compile) are all baked into the emitted source. Re-running a compiled module hits the disk-cached embeddings and the precomputed rotations on second-and-later runs.

Figure 1: Sutra compilation pipeline. Stages 1–4 run at compile time; stage 5 is the runtime forward pass. The boundary between compile-time and runtime is exactly where neural-network training versus inference draws the line — pre-execution work becomes constants in the running graph.

   source code  (.su)
        │
        │   (1) lex + parse
        ▼
   AST   (Call / Var / Function / ClassDecl nodes)
        │
        │   (2) inline stdlib + egglog simplify
        │       (bind, bundle, similarity → primitive tensor ops)
        ▼
   simplified AST   (residual: leaf tensor-op composition)
        │
        │   (3) codegen
        │       (emit Python module + inline _VSA class source)
        ▼
   Python module text   (self-contained, no Sutra-runtime import)
        │
        │   (4) compile-time substrate population
        │       embed_batch · prewarm_rotation_cache · populate_sutradb
        ▼
   warm runtime   (module loaded, .sdb codebook, cached R_role tensors)
   ──── compile time ────────────────────────────────────────────────
   ────── runtime ───────────────────────────────────────────────────
        │
        │   (5) forward pass on input tensors
        ▼
   output vector → nearest_string lookup → label

The dividing line is load-bearing for the program-network identity: by the time stage 5 begins, every role rotation, codebook entry, and stdlib reduction has been resolved to a constant tensor or a primitive op, exactly as a feed-forward neural network's weights are constants by inference time.

4.1 Substrate-purity invariants

Three invariants the compiler enforces: (1) every primitive runs on the substrate (numpy is allowed only at compile time for codebook construction and rotation pre-warm, never on the runtime hot path); (2) no scalar extraction inside an operation — operations may not unpack a Python float from a substrate vector, do scalar arithmetic, and pack the result back; (3) no Python control flow inside an operation — loop halt uses substrate primitives (heaviside, saturate_unit) instead of Python ternaries.

4.2 Compile-time resolution to tensor normal form

The central compile-time mechanism that lets the compiler achieve tensor normal form is precomputed rotation matrices: every role rotation is constructed at compile time (prewarm_rotation_cache) and stored as a constant tensor. At runtime, bind(role, filler) is a single matmul against a precomputed matrix — the compile-time resolution eliminates the QR construction from the runtime graph entirely. Role rotations are constants from the runtime's perspective, the same way neural-network weights are constants at inference time. With torch.compile (opt-in via SUTRA_TORCH_COMPILE=1), the tracer further folds the per-tick loop body into a single fused kernel.

4.3 A worked lowering

A two-field bundled record encode2(r_a, f_a, r_b, f_b) := bundle(bind(r_a, f_a), bind(r_b, f_b)) lowers in five stages (parse → stdlib beta-substitution → compile-time RotationFor resolution → peephole fusion to _VSA.bundle_of_binds → leaf tensor ops einsum + linalg.norm + divide) over rotations materialized at compile time. Appendix G traces each stage with the residual after every reduction. The bottom of the chain contains no bind/bundle/normalize symbol and no Python control flow; surface lambda calculus and runtime tensor arithmetic are two notations for the same computation.

5. Demonstration corpus

The smoke test (examples/_smoke_test.py) runs 10 demonstration programs end-to-end (hello-world, fuzzy branching, role-filler record, classifier, analogy, knowledge graph, predicate lookup, fuzzy dispatch, nearest-phrase retrieval, sequence reduction) across 27 .su files in examples/. Loop coverage lives in examples/do_while_adder.su and the 23-case test_loop_function_decl.py suite. Each program exercises a different language feature; the §3.6 differentiable-training experiment uses the same primitive set those programs are built from.

6. Limitations and Future Work

6.1 Codebook integration depth

The embedded codebook store covers the compile-time embed → runtime decode path today. Extended features (hashmap routing, persistent codebook across runs via SUTRA_DB_PATH) are deferred until there is a concrete requirement beyond the current demonstration corpus.

7. Conclusion

Sutra demonstrates that a programming language whose compile target is a single tensor-op graph over a frozen embedding substrate is a tractable design — not a research thought experiment but a working compiler with running demonstration programs. The design choice that makes it tractable is uniform shape: every value is the same vector layout, every operation is one tensor op, the compiler treats the whole program as a dataflow graph with no type dispatch at the leaves.

The substrate-purity story is what makes the language useful for the empirical question we built it to address: which embedding operations actually compose, at what capacity, on which substrates. With the language in hand, those questions become programs to write rather than scripts to glue together.

References

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Appendix

Appendix G — Worked lowering of a two-field bundled record

The body §4.3 sketches the lowering of encode2(r_a, f_a, r_b, f_b) := bundle(bind(r_a, f_a), bind(r_b, f_b)). Here we trace each stage with the explicit residual.

Stage 1 — AST after parse. A tree of Call nodes over named identifiers: Call("bundle", Call("bind", r_a, f_a), Call("bind", r_b, f_b)).

Stage 2 — beta reduction by stdlib inlining. bind, bundle, and normalize are stdlib functions: bind(r,f) ≡ RotationFor(r) @ f, bundle(x,y) ≡ normalize(x+y), normalize(v) ≡ v / (‖v‖ + ε). After substitution the body becomes normalize(RotationFor(r_a) @ f_a + RotationFor(r_b) @ f_b). No bind or bundle symbol remains; the residual is straight- line algebra over four tensor primitives.

Stage 3 — compile-time constant resolution. RotationFor(r) is a compile-time function returning R = QR(seed = hash(r))[Q]. The compiler evaluates it for each role at compile time, freezes the results as constant tensors R_a and R_b, and stores them in the rotation cache. The body becomes normalize(R_a @ f_a + R_b @ f_b) — R_a and R_b are now load-bearing constants in the same sense as the weight matrices of a feed-forward network.

Stage 4 — peephole fusion. The simplifier recognizes normalize(Σᵢ Rᵢ @ fᵢ) as the bundle-of-binds pattern and rewrites it to _VSA.bundle_of_binds([(R_a, f_a), (R_b, f_b)]) — one kernel launch instead of two matmuls + add + norm.

Stage 5 — leaf tensor ops at runtime. bundle_of_binds stacks rotations into a (k, d, d) tensor, stacks fillers into (k, d), runs one batched einsum + sum + L2-normalize:

encode2 ≡ v / (‖v‖ + ε)
where  v = einsum("kij,kj->i", stack([R_a, R_b]), stack([f_a, f_b]))

The compiled forward pass for encode2 is exactly those three torch calls — einsum, linalg.norm, divide — over precomputed R_a, R_b and runtime-supplied f_a, f_b.