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# RuVector WASM Integration: Algorithmic Frontiers & Crate Synthesis
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**Document ID**: wasm-integration-2026/00-executive-summary
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**Date**: 2026-02-22
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**Status**: Research Complete
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**Classification**: Strategic Technical Research
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**Workspace**: RuVector v2.0.3 (85+ crates, Rust 2021 edition)
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---
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## Thesis
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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.
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---
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## Research Documents
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| # | Document | Focus |
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|---|----------|-------|
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| 01 | [Pseudo-Deterministic Min-Cut](./01-pseudo-deterministic-mincut.md) | Canonical min-cut as coherence gate primitive |
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| 02 | [Sublinear Spectral Solvers](./02-sublinear-spectral-solvers.md) | Laplacian solvers, spectral coherence scoring |
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| 03 | [Storage-Based GNN Acceleration](./03-storage-gnn-acceleration.md) | AGNES hyperbatch, cold-tier graph streaming |
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| 04 | [WASM Microkernel Architecture](./04-wasm-microkernel-architecture.md) | Verifiable cognitive container design |
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| 05 | [Cross-Stack Integration Strategy](./05-cross-stack-integration.md) | Unified roadmap, dependency mapping, ADR proposals |
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---
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## Key Findings
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### 1. Canonical Min-Cut as Coherence Gate
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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`.
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**Affected crates**: `ruvector-mincut`, `ruvector-attn-mincut`, `cognitum-gate-kernel`, `cognitum-gate-tilezero`
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### 2. Spectral Coherence via Sublinear Solvers
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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.
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**Affected crates**: `ruvector-solver`, `ruvector-solver-wasm`, `ruvector-coherence`, `prime-radiant`, `ruvector-math`
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### 3. Storage-Efficient GNN Training
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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.
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**Affected crates**: `ruvector-gnn`, `ruvector-gnn-wasm`, `ruvector-gnn-node`, `ruvector-graph`
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### 4. WASM Microkernel = Verifiable Cognitive Container
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RuVector already has the components for a deterministic WASM microkernel:
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- `cognitum-gate-kernel`: no_std, 64KB tiles, bump allocator, delta-based graph updates
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- `ruvector-wasm`: kernel-pack system with Ed25519 verification, SHA256, epoch budgets
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- `ruvector-solver-wasm`: O(log n) math in WASM
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- `ruvector-mincut-wasm`: dynamic min-cut in WASM
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The missing piece is **stitching these into a single sealed container** with a canonical witness chain.
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### 5. Sublinear Matching Bounds Inform Detector Design
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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`.
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---
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## Crate Dependency Map
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```
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ruvector-core
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├── ruvector-graph ──────────────── ruvector-graph-wasm
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│ └── ruvector-mincut ─────────── ruvector-mincut-wasm
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│ ├── ruvector-attn-mincut
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│ └── cognitum-gate-kernel ── (no_std WASM tile)
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│ └── cognitum-gate-tilezero (arbiter)
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├── ruvector-gnn ────────────────── ruvector-gnn-wasm
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├── ruvector-solver ─────────────── ruvector-solver-wasm
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├── ruvector-coherence
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├── ruvector-sparse-inference ───── ruvector-sparse-inference-wasm
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├── prime-radiant
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└── ruvector-wasm (unified WASM bindings + kernel-pack)
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```
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---
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## Quantitative Impact Projections
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| Primitive | Current State | Post-Integration | Speedup | WASM-Ready |
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|-----------|--------------|------------------|---------|------------|
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| Min-cut gate | Randomized, non-canonical | Pseudo-deterministic, canonical | 1.5-3x static, 10x dynamic | Yes (cognitum-gate-kernel) |
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| Coherence score | Dense Laplacian O(n^2) | Spectral O(log n) | 50-600x at 100K nodes | Yes (ruvector-solver-wasm) |
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| GNN training | RAM-bound, batch | Hyperbatch streaming, cold-tier | 3-4x throughput | Partial (mmap not in WASM) |
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| Drift detection | Oblivious sketches | Adaptive query patterns | 2-5x precision | Yes |
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| Witness chain | Per-tile fragments | Canonical, hash-chained | Deterministic | Yes (kernel-pack Ed25519) |
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---
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## Strategic Recommendations
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1. **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.
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2. **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`.
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3. **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.
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4. **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.
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5. **Ongoing**: Track sublinear matching lower bound results to refine adaptive detector design in coherence scoring modules.
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---
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## Vertical Alignment
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| Vertical | Primary Primitive | Differentiator |
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|----------|------------------|----------------|
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| Finance (fraud, risk) | Canonical min-cut | Auditable structural safety gates |
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| Cybersecurity | Spectral coherence | Real-time network fragility detection |
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| Medical/Genomics | Cold-tier GNN | Large-scale genomic graph training |
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| Regulated AI | WASM container | Deterministic, witnessable decisions |
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| Edge/IoT | All four | Sub-10ms on ARM, no server required |
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---
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## Document Series Navigation
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- **Next**: [01 - Pseudo-Deterministic Min-Cut](./01-pseudo-deterministic-mincut.md)
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- **Full index**: This document
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