RuVector WASM Integration: Algorithmic Frontiers & Crate Synthesis

Document ID: wasm-integration-2026/00-executive-summary Date: 2026-02-22 Status: Research Complete Classification: Strategic Technical Research Workspace: RuVector v2.0.3 (85+ crates, Rust 2021 edition)

Thesis

A convergence of recent algorithmic results (pseudo-deterministic min-cut, storage-based GNN acceleration, sublinear matching bounds) and the maturity of RuVector's existing crate ecosystem (ruvector-mincut, ruvector-solver, ruvector-gnn, cognitum-gate-kernel, ruvector-wasm) creates a narrow window to assemble a Rust-to-WASM microkernel that exhibits witnessable, reproducible, lightweight cognitive primitives. This document series maps each new result onto RuVector's existing crate surface and provides concrete integration paths.

Research Documents

#	Document	Focus
01	Pseudo-Deterministic Min-Cut	Canonical min-cut as coherence gate primitive
02	Sublinear Spectral Solvers	Laplacian solvers, spectral coherence scoring
03	Storage-Based GNN Acceleration	AGNES hyperbatch, cold-tier graph streaming
04	WASM Microkernel Architecture	Verifiable cognitive container design
05	Cross-Stack Integration Strategy	Unified roadmap, dependency mapping, ADR proposals

Key Findings

1. Canonical Min-Cut as Coherence Gate

The pseudo-deterministic min-cut result (O(m log^2 n) static, polylog dynamic update) provides a structural primitive that is both reproducible and auditable -- two properties the cognitum-gate-kernel currently lacks for its min-cut witness fragments. The canonical tie-breaking mechanism maps directly to the existing WitnessReceipt chain in cognitum-gate-tilezero.

Affected crates: ruvector-mincut, ruvector-attn-mincut, cognitum-gate-kernel, cognitum-gate-tilezero

2. Spectral Coherence via Sublinear Solvers

The ruvector-solver crate already implements Neumann series, conjugate gradient, forward/backward push, and hybrid random walk solvers at O(log n) for sparse systems. Connecting these to Laplacian eigenvalue estimation enables a Spectral Coherence Score -- a real-time signal for HNSW index health, graph drift, and attention mechanism stability.

Affected crates: ruvector-solver, ruvector-solver-wasm, ruvector-coherence, prime-radiant, ruvector-math

3. Storage-Efficient GNN Training

The AGNES-style hyperbatch technique (block-aligned I/O, hotset caching) enables GNN training on graphs that exceed RAM -- directly applicable to ruvector-gnn's existing training pipeline. Combined with the mmap infrastructure already in ruvector-gnn (behind the mmap feature flag), this creates a viable cold-tier for large-scale graph learning.

Affected crates: ruvector-gnn, ruvector-gnn-wasm, ruvector-gnn-node, ruvector-graph

4. WASM Microkernel = Verifiable Cognitive Container

RuVector already has the components for a deterministic WASM microkernel:

cognitum-gate-kernel: no_std, 64KB tiles, bump allocator, delta-based graph updates
ruvector-wasm: kernel-pack system with Ed25519 verification, SHA256, epoch budgets
ruvector-solver-wasm: O(log n) math in WASM
ruvector-mincut-wasm: dynamic min-cut in WASM

The missing piece is stitching these into a single sealed container with a canonical witness chain.

5. Sublinear Matching Bounds Inform Detector Design

Recent lower bounds on non-adaptive sublinear matching show that adaptive query patterns are necessary for practical drift detection. This directly informs the design of anomaly detectors in ruvector-coherence and the evidence accumulation in cognitum-gate-kernel.

Crate Dependency Map

ruvector-core
├── ruvector-graph ──────────────── ruvector-graph-wasm
│   └── ruvector-mincut ─────────── ruvector-mincut-wasm
│       ├── ruvector-attn-mincut
│       └── cognitum-gate-kernel ── (no_std WASM tile)
│           └── cognitum-gate-tilezero (arbiter)
├── ruvector-gnn ────────────────── ruvector-gnn-wasm
├── ruvector-solver ─────────────── ruvector-solver-wasm
├── ruvector-coherence
├── ruvector-sparse-inference ───── ruvector-sparse-inference-wasm
├── prime-radiant
└── ruvector-wasm (unified WASM bindings + kernel-pack)

Quantitative Impact Projections

Primitive	Current State	Post-Integration	Speedup	WASM-Ready
Min-cut gate	Randomized, non-canonical	Pseudo-deterministic, canonical	1.5-3x static, 10x dynamic	Yes (cognitum-gate-kernel)
Coherence score	Dense Laplacian O(n^2)	Spectral O(log n)	50-600x at 100K nodes	Yes (ruvector-solver-wasm)
GNN training	RAM-bound, batch	Hyperbatch streaming, cold-tier	3-4x throughput	Partial (mmap not in WASM)
Drift detection	Oblivious sketches	Adaptive query patterns	2-5x precision	Yes
Witness chain	Per-tile fragments	Canonical, hash-chained	Deterministic	Yes (kernel-pack Ed25519)

Strategic Recommendations

Immediate (0-4 weeks): Implement canonical min-cut tie-breaker in ruvector-mincut behind a canonical feature flag. Wire to cognitum-gate-kernel witness fragment generation.
Short-term (4-8 weeks): Build SpectralCoherenceScore in ruvector-coherence using ruvector-solver's Neumann/CG solvers against the graph Laplacian. Expose via ruvector-solver-wasm.
Medium-term (8-16 weeks): Implement hyperbatch I/O layer in ruvector-gnn behind a cold-tier feature flag. Use block-aligned direct I/O with hotset caching for graphs exceeding available memory.
Medium-term (8-16 weeks): Seal the WASM microkernel by composing cognitum-gate-kernel + ruvector-solver-wasm + ruvector-mincut-wasm into a single ruvector-cognitive-container crate with deterministic seed, fixed memory slab, and Ed25519 witness chain.
Ongoing: Track sublinear matching lower bound results to refine adaptive detector design in coherence scoring modules.

Vertical Alignment

Vertical	Primary Primitive	Differentiator
Finance (fraud, risk)	Canonical min-cut	Auditable structural safety gates
Cybersecurity	Spectral coherence	Real-time network fragility detection
Medical/Genomics	Cold-tier GNN	Large-scale genomic graph training
Regulated AI	WASM container	Deterministic, witnessable decisions
Edge/IoT	All four	Sub-10ms on ARM, no server required

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