Verifying a Neuro-Symbolic Substrate by Reduction to Tensor Normal Form

Abstract

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 argue that Sutra, a typed purely-functional language whose compiled forward pass is a tensor-op graph, changes the shape of the problem for the non-learned part of a system. Every Sutra program β-reduces to a tensor normal form (TNF): one fused tensor-op graph over a frozen substrate. Crucially, the construct that makes conventional verification expensive — the branch — disappears: if/else reduces to a single three-valued-Kleene polynomial, Lagrange-interpolated and exact on the {−1, 0, +1} truth grid, and each loop to a bounded soft-halt recurrence. Because branches are polynomials rather than forks, the path set does not explode — at a cost we quantify rather than hide: the per-branch polynomial grows in size with branch-nesting depth (§3.4). Verifying the trusted base — kernel roles and named critical programs — reduces from imperative-path enumeration to discharging a finite set of closed-form obligations over a small, fixed set of tensor graphs, with program equivalence handled by algebraic rewriting of the reduced graphs (a fixed set of sound rewrites — constant-folding, zero- absorption, CSE — that canonicalises the equivalences it covers, not yet a complete decision procedure).

We make this precise as three per-construct obligation families (contract / branch-range / termination), and we ground the claim that the reduced form is faithfully computable on the empirics already measured for the substrate: rotation binding decodes bundles at 100% accuracy through width k = 8 on four frozen embedding substrates (where the Hadamard baseline has collapsed to 2.5–7.5%), with a reversibility round-trip of 1.5 × 10⁻¹⁵; and a downstream OS (Yantra) runs full arithmetic — operator selection included — bit-exact through its kernel (18/18; 1024/1024 symbol round-trips at max |err| = 0.0). We are explicit about the boundary: this covers the non-AI trusted base, per published contract, not the whole running system and not anything riding on a learned weight. Three obligation families already have working mechanical checks that run on the substrate — Kleene-gate exactness (worst error 0.0 across the truth grid), connective range-soundness (outputs provably in [−1, +1] over the whole fuzzy domain), and loop termination (bounded + monotone halt) — plus the kernel-enforced role-isolation half of the contract obligation. The general polynomial-obligation checker that would discharge an arbitrary obligation is specified but not yet built; what is built is the per-construct discharge for each family above. The contribution is a verification framework and reduction with its first obligations mechanically discharged, an explicit account of the costs (§3.4) and the boundary (§5), and the argument that the reduction is real rather than rhetorical.

1. Introduction

Two facts are usually taken to be in tension. (i) Critical systems want formal guarantees about their trusted base. (ii) Useful systems increasingly contain learned components, which resist formal guarantees. The common resolution is to verify neither — the imperative trusted base is too large to verify cheaply, and the learned part is given up on, so the whole stack ships on testing alone.

Sutra offers a different decomposition. It is a typed, purely functional language whose compiler β-reduces an entire program — primitives, control flow, string I/O — to a single fused tensor-op graph over a frozen embedding substrate (paper/paper.md). We call that reduced artefact the program's tensor normal form (TNF). Our claim is narrow and structural:

For the non-learned trusted base, reduction to TNF turns verification from control-flow path enumeration into algebra over a small fixed set of tensor graphs.

This does not make the learned parts safe. It makes them separable: the boundary between "reduces to a checkable tensor graph" and "depends on a learned weight" is syntactically visible, so the trusted base can be verified while the learned part is quarantined behind contracts and monitoring.

Contributions.

1. The reduction (§2): why TNF is a normal form and not constant folding, and why equivalence on the reduced graph is algebra.
2. The obligation framework (§3): three per-construct obligation families — contract, branch-range, termination — that a checker would discharge. The branch-range family (§3.2), built on three-valued polynomial Kleene logic, is the one that removes path explosion: branches become closed-form polynomials, not forks, so the cost of conditionals stops being exponential.
3. The faithfulness evidence (§4): the measured substrate exactness that makes the reduction meaningful rather than rhetorical, including a downstream OS computing bit-exactly through its kernel.
4. The boundary (§5): a precise statement of what is not covered, in the DO-178C-shaped framing of a real certification effort.

We claim a framework and a reduction, not a certified system. §5 is as load- bearing as §2.

2. Tensor normal form as a normal form

A TNF is not a constant-folded version of an otherwise-conventional program. There is no residual program underneath it. In a neural network the weights are matrices and the forward pass is chained matmuls; nobody calls that an optimization of some more primitive program, because the matrices are the computation. Sutra does the same for arbitrary programs: compilation produces the weight/rotation structure, and execution is the forward pass.

The distinction from the classical specialization spectrum — constant propagation, partial evaluation (Futamura projections), staging — is ontological, not quantitative. Those transforms remove known subexpressions from a program that still runs in a conventional model; TNF collapses the model itself into linear algebra, leaving nothing un-folded to fall back on.

The verification-relevant consequence: equivalence checking moves onto the reduced graph as algebraic rewriting, not a traversal of possible executions. Concretely, the compiler's simplifier applies a fixed set of sound rewrites — each an exact algebraic identity or soundness-preserving structural match (no approximate rewrites): a − a → 0 and zero-absorption, arithmetic constant folding (x + 0 → x, …), a displacement-addition bundle rewrite, and common- subexpression elimination. Two programs that differ only by equivalences this rewrite set captures reduce to the same graph, so checking those equivalences is algebra, not path enumeration.

Honest scope (this answers a fair reviewer objection). This is not a claim that the simplifier is a complete decision procedure for program equivalence: it is a confluent rewrite set with documented non-rewrites (e.g. it does not materialise composite rotations), so there exist equivalent programs it does not collapse to an identical graph. A complete canonical form is future work; what we claim, and what the rewrite set delivers, is that equivalence checking is moved into algebra over the reduced graph and is exact for the rewrites it implements. The §3.2/§3.3 obligation discharges below do not depend on full canonicalisation — they bound and check individual reduced graphs directly.

3. The obligation framework

Reduction concentrates all verification load into three closed-form families, one per Sutra construct that survives into the TNF.

3.1 Contract obligations (from β-reduction). Each trusted program carries an axon-typed contract: the input roles it may read, the output roles it may write, and its status conditions. For program p with contract C, the obligation is that TNF(p) reads only C.read_roles, writes only C.write_roles, and that the role-to-role function it computes is the one C specifies. The compiler already emits the static read/write key sets (AXON_KEYS_READ, AXON_KEYS_BOUND) that seed the role half of this obligation.

The read/write confinement part of this obligation is discharged at the kernel (the downstream OS): a program can only emit on roles in its write_roles (capability-checked at routing) and is delivered only axons on roles in its read_roles, with no cross-role leakage — mechanically tested (three kernel tests, incl. a two-role read-isolation check). Two parts remain open and are the harder ones: that the role-to-role function matches C (program correctness, not just confinement), and that the static AXON_KEYS_READ/BOUND analysis is sound against the keys the program actually touches. "Confinement discharged" is not "contract obligation done."

3.2 Branch-range obligations (from polynomial Kleene logic). This family carries most of the weight, because branches are what make conventional verification expensive: each if/else doubles the path set, so a trusted base with b branches presents up to 2ᵇ paths — the state-space explosion that imperative verification has to fight. Sutra removes the branch as a control-flow object. Source if/else reduces to a single polynomial that interpolates between the branch values on a fuzzy truth value; the connectives are the three-valued Kleene operators (and, or, not, the t-norms) realised as Lagrange-interpolated polynomials exact on the 3×3 Kleene grid over {−1 = false, 0 = unknown, +1 = true}, branchless and smooth (hence gradient- compatible) off the grid.

Two consequences matter for verification. First, branchlessness collapses the path set: a branch is no longer a fork to enumerate but a polynomial whose value the truth-axis scalar determines, so the obligation is a closed-form bound on that polynomial's range and sign over [−1, +1] — a polynomial extremum/root problem, not a path walk. Second, three-valued rather than Boolean is the right logic for a substrate that mixes exact symbolic and uncertain learned signals: the middle value (unknown) is first-class, so the verifier reasons about "undetermined" directly instead of forcing a premature Boolean collapse, while the crisp true/false cases stay bit-exact because the interpolation is exact on the grid. A finite nine-point check (the polynomial reproduces the Kleene table at {−1, 0, +1}²) anchors the smooth form to the discrete logic it stands in for.

This grid-exactness obligation is now discharged mechanically (the first FV obligation to move from stated to checked). A test compiles the real pipeline — parse → inline the polynomial → simplify → tensor-op codegen → runtime — and evaluates &&, ||, ! at all nine grid points on the substrate, asserting the Kleene strong-logic table (and = min, or = max, not = negate on the antipodal encoding). Measured: worst |error| = 0.0 across the grid — exact, not approximate. The polynomials checked are the ones the compiler emits: a&&b = (a+b+ab−a²−b²+a²b²)/2, a||b = (a+b−ab+a²+b²−a²b²)/2, !a = −a.

The off-grid branch-range obligation is also discharged. Off the grid the polynomials interpolate (they do not reproduce min/max exactly there — that is the intended C^∞ behaviour between grid points); what soundness requires is that they never produce an out-of-range "truth" value. Measured on a dense sweep of the continuous fuzzy domain [−1, +1]²: the connective outputs stay in [−1.000000, +1.000000] — no over/undershoot anywhere. So the connectives are valid truth-axis operations across the whole domain, not just at the grid. (Both checks: sdk/sutra-compiler/tests/test_fv_kleene_grid_exactness.py.)

The same grid saturation makes selection exact in practice: a sufficiently sharpened softmax select is a true one-hot, because exp(−k) underflows to exactly 0 (float32 for modest k; far below ulp in float64), so unselected branches are multiplied by exact zero rather than a small residue — the mechanism behind the bit-exact operator dispatch in §4.3.

3.3 Termination obligations (from soft-halt loops). Each loop is a bounded recurrence state ← R · state with a fixed-width state vector and a halt cell. Termination reduces to "the halt signal is monotone within bounded steps," discharged per loop — far smaller than proving an arbitrary while terminates.

This obligation is discharged, structurally and observably. Structurally the emitted loop is for _t in range(max_iters) (bounded by construction, no unbounded while) and halted = min(halted + halt, 1) with halt = sigmoid(·) ≥ 0 (monotone non-decreasing, capped at 1; on saturation state = (1−halted)·cand + halted·state freezes). Observably (torch substrate): a non-converging loop runs to the bound and stops (iters_active = 9.998/10, never exceeding max_iters — bounded, no hang); a converging loop is exactly frozen across unroll depth — its state at T=20 equals its state at T=10, diff = 0.0 — so the monotone cumulative halt, once saturated, holds. Check: sdk/sutra-compiler/tests/test_fv_termination.py.

Discharging §3.2 needs a bespoke checker: off-the-shelf SMT solvers target Boolean and linear arithmetic, not the polynomial obligations TNF produces. The methodology is not "feed it to an SMT solver and hope." For the obligations we discharge here it is concrete and finite: grid-exactness is a nine-point evaluation; range-soundness is a bound on a low-degree polynomial over a box, obtained by checking the finite set of critical points (box corners plus interior stationary points where the gradient vanishes) — closed-form, not search; loop termination is structural plus a saturation observation. The general checker that would discharge an arbitrary reduced-graph obligation is the bulk of the remaining work and is not built.

3.4 The cost: expression size and numerical stability. Removing the branch as a control-flow object is not free, and the honest accounting matters. We trade path explosion for expression-size growth. Conventional verification faces up to 2ᵇ paths in the number of branches b; the polynomial encoding has no path set, but a conditional whose guard is itself a conditional composes a degree-2(-per-variable) polynomial into another, so the polynomial degree can grow with branch-nesting depth d (roughly 2ᵈ without intervention). The two explosions are in different parameters: b (total branch count) versus d (nesting depth), and in practice d ≪ b — most branches are shallow — so the trade is usually favourable, but it is a trade, not a free lunch. There is a real mitigation native to the substrate: defuzzification (is_true/snap) between nesting levels polarises a value back toward the {−1, 0, +1} grid, which caps the degree that propagates into the next level rather than letting it compound; the cost is then paid in defuzz iterations instead of degree. Numerical stability: we have measured exactness for the single connectives (worst error 0.0 on the grid, range in [−1, +1]) and for full arithmetic through a downstream kernel (§4), but the float behaviour of deeply nested, un-defuzzified high-degree compositions is not yet characterised — quantifying it (and the degree at which conditioning degrades) is open work, flagged here rather than waved past.

4. Faithfulness: the reduction is computed exactly

A reduction to algebra is only worth anything if the substrate computes the reduced form exactly. Three measured results show it does. The protocol and full tables are in the Sutra language paper; we restate enough here that this paper stands on its own.

4.1 Bundle decoding. Protocol: for each bundle width k, bind k role–filler pairs by rotation, superpose (bundle) them into one vector, and decode each filler by unbind + nearest-codebook (argmax-cosine); accuracy is the fraction recovered, 10 trials per width. Result: rotation binding decodes at 100% accuracy through width k = 8 on four frozen substrates spanning two modalities — three text encoders (nomic-embed-text, all-minilm, mxbai-embed-large) and the ESM-2 protein model — where the textbook Hadamard (element-wise) binding has already collapsed at k = 8 (2.5% on mxbai-embed-large, 7.5% on all-minilm). The point for verification: the bundle/bind/unbind primitives the TNF is built from recover their inputs exactly at the widths the trusted base uses.

4.2 Reversibility. A single bind+unbind cycle returns the input at the floating-point noise floor: mean ‖unbind(R, bind(R, x)) − x‖ = 1.5 × 10⁻¹⁵ across all four substrates — i.e. the rotation is invertible to machine epsilon, so a reduced graph built from binds/unbinds does not silently lose information.

4.3 Exactness through a real trusted base. A downstream GPU-native OS (Yantra) runs full arithmetic expressions on the Sutra substrate through its kernel — operator selection included, decided on the substrate by a saturated select (§3.2) rather than a host branch — and recovers results bit-exact within the float32 exact-integer range (18/18 operator-dispatch cases at |err| = 0.0, including the 2²⁴ boundary), with 1024/1024 distinct symbols round- tripped through the kernel router at max |err| = 0.0. This is the §3.1 contract property in miniature: the reduced graph computes exactly what the source denotes, end-to-end through a kernel.

These are existence results for exactness, not a proof that every TNF is exact; they establish that the reduction's premise holds on the substrates and programs measured.

5. What this does and does not buy — the boundary

The reduction buys the shape of a certification effort, DO-178C-style: a fixed image and fixed critical-program set (Plan); axon-typed contracts (Requirements); Sutra source whose TNFs are the designs (Design); mechanical proofs that TNFs meet contracts plus discharged polynomial obligations (Verification artefacts); an append-only capability/admission log (Trace); and the compiler in scope for qualification with its TNF output — not the source — as the artefact under review (Tooling assurance).

It does not buy the following, and saying so is part of the method:

• No whole-system proof. Equivalence-as-algebra applies to the contract surface of individual programs whose TNFs are individually tractable. It does not dissolve state-space explosion across a running system; no real system has a closed-form whole-system proof and we claim none.
• No verification of the learned parts. Anything that invokes an embedding model or depends on a learned weight is outside the trusted base. It gets bounded behaviour, capability discipline, provenance, and runtime monitoring. The reduction makes the learned parts quarantinable, not safe.
• No certified artefact today. The architecture is verification-friendly; the proofs are an ongoing project and the polynomial-obligation checker is not built. A certified configuration would be per-customer, per-mission.

6. Related work

TNF sits beyond the classical specialization spectrum (§2): constant propagation, partial evaluation / Futamura projections, and staging all leave a residual program in a conventional model, whereas TNF collapses the model into linear algebra. The certification framing follows DO-178C's plan/requirements/design/ verification/trace structure. The substrate primitives are vector-symbolic architectures (binding, bundling, cleanup); Sutra's contribution there, on which this work rests, is that rotation binding remains exact through bundle widths where the standard Hadamard binding has collapsed (paper/paper.md).

7. Conclusion

Reducing the non-learned trusted base to a tensor normal form changes formal verification from imperative-path enumeration into algebra over a small fixed set of tensor graphs, with the verification load concentrated into three closed-form obligation families. This is not only a reduction on paper: three of the families already have working, measured mechanical checks — Kleene-gate exactness (worst error 0.0), connective range-soundness (outputs in [−1, +1]), and loop termination — plus the kernel-enforced role-isolation half of the contract obligation. The premise that the reduced form is computed exactly is borne out by the measured substrate exactness, including a downstream OS that computes bit-exactly through its kernel. What remains is the general polynomial- obligation checker (the per-construct discharges exist; the arbitrary-obligation tool does not), the harder halves of the contract obligation (function correctness, static-key soundness), and characterising the numerical cost of deep branch-nesting (§3.4). The contribution is the reduction, the framework, and its first obligations discharged — with an explicit boundary around the learned parts the method deliberately does not touch.

Companion spec (obligations stated for implementation): planning/sutra-spec/formal-verification.md. Substrate empirics: paper/paper.md. Downstream OS verification surface: Yantra paper/paper.md §4.