ruvnet

Agentic Validation System

A three-layer regression-protection stack for AI-built codebases that ship fast across many small fixes. Designed for projects where:

• Releases are frequent (daily alphas, not monthly majors)
• Fixes are small (one-line CLI parser swaps, dependency moves) but many in number
• Multiple agents touch overlapping code in the same release window
• A regression that ships affects every user immediately, not just one customer

The stack catches three distinct regression classes that traditional CI misses, then provides forensic tools to answer "when did this break and what changed?"

ruvnet

Pattern fill, without training

A small Rust library that copies the style of a few example sequences and produces new ones in the same shape — without training a model.

You give it a handful of examples (Mario level slices, drum loops, snippets of structured text — any short tokens that have a pattern). It reads them once. From then on it can produce new sequences that look like they came from the same source. No GPUs. No PyTorch. No model files. Just Rust.

ruvnet

Sparse-Mario

A 2,200-line Rust example that uses a subquadratic attention kernel — built for edge LLM inference on Raspberry Pi Zero 2W — as a training-free Super Mario Bros level generator. The same kernel runs in two modes from one binary:

• Autoregressive — token-by-token retrieval LM, walks the corpus's empirical bigram statistics. Now incremental via KvCache + decode_step: 2,880× faster than the original full-forward path (25 s → 9 ms for a 14×50 grid).
• Masked discrete diffusion — bidirectional context, iterative denoising with a MaskGIT cosine schedule. SOTA on this artifact: 3.8× lower L2 distance to corpus than a 1st-order Markov bigram baseline, 6.9× lower than the autoregressive path itself.

No autograd. No learned weights. No Python in the loop. The Mario corpus is the model.

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ruvnet

You are a scheduled nightly research agent for the ruvector project. Produce deep state-of-the-art research on practical-to-exotic applications and improvements for ruvector, deliver a new feature branch with WORKING RUST code, a detailed ADR, a research document, and publish a public GitHub gist overview.

CONSTRAINTS (absolute):

• RUST ONLY. No Python, JS, TS, or anything beyond glue shell.
• NO mocks, NO TODO stubs, NO placeholder benchmarks — real cargo-run numbers only.
• Files under 500 lines. Never commit secrets. Never save to repo root.
• Use /docs for docs, /crates or /examples for code, /docs/adr for ADRs.

STEP 1 — ORIENT

• git fetch origin && git checkout main && git pull

ruvnet

ruvector 2026: MUVERA FDE — High-Performance Rust Multi-Vector Late-Interaction Search

150-word summary: ruvector now ships MUVERA Fixed Dimensional Encoding (NeurIPS 2024) as a pure Rust crate for ColBERT-style multi-vector retrieval. FDE converts O(n×T_q×T_d×D) brute-force MaxSim into a single dot-product scan, delivering 9.5× QPS improvement over brute-force at n=10K documents. Benchmark: 19 QPS vs 2 QPS (exact MaxSim oracle), x86-64 Linux, cargo --release. Three index variants — CentroidIndex, MaxSimIndex (oracle), MuveraFdeIndex — plus a two-stage FDE+Rerank pipeline.

Introduction: The Multi-Vector Search Gap in 2026

ColBERT, ColPali, and BGE-M3 have made late-interaction retrieval the dominant paradigm for precision-critical RAG pipelines. Each document is represented as T token embeddings rather than a single vector. The MaxSim score — Σ_i max_j dot(q_i, d_j) — captures nuanced semantic overlap that single-vector cosine similarity misses entirely.

The problem: scoring one query aga

ruvnet

ruvector 2026: LoRANN — High-Performance Rust Vector Search with Per-Cluster SVD Score Approximation

30.9× QPS speedup over brute-force at 56% recall@10 on 50K vectors, 54.9× at moderate recall — pure Rust, no BLAS, no Python.

ruvector now implements LoRANN (NeurIPS 2024) — a clustering-based approximate nearest-neighbour index that replaces the expensive per-cluster exact scorer with a compact rank-r SVD factorisation, achieving massive throughput gains while remaining production-deployable on commodity hardware.

Branch: research/nightly/2026-05-08-lorann · PR: #444

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ruvnet

ruvector 2026: SymphonyQG — High-Performance Rust Vector Search with Graph-Coupled 4-bit FastScan

ruvector is a high-performance Rust vector search library. This research spike implements SymphonyQG (SIGMOD 2025) — the first graph-coupled FastScan ANN implementation in pure Rust, achieving 4–10× faster neighbor scoring than exact f32 distance computation.

Introduction

Approximate Nearest Neighbor (ANN) search powers production RAG pipelines, semantic

ruvnet

ruvector 2026: MUVERA Multi-Vector Retrieval — High-Performance Rust ColBERT Search

150-char summary: ruvector-muvera brings ColBERT-style late-interaction retrieval to Rust: 42× faster than brute-force MaxSim, zero bespoke infrastructure, pure safe Rust.

Introduction

Modern neural search is dominated by late-interaction retrieval models like ColBERT, ColBERT v2, and PLAID. Instead of compressing a 200-word document into a single vector, these models produce one high-dimensional embedding per token — capturing fine-grained semantics that bi-encoders discard. The result: 3–7% better nDCG@10 on BEIR benchmarks over state-of-the-art bi-encoders like E5-large and text-embedding-3.

The catch? Searching 1 million documents requires scoring 16 query tokens × 200 doc tokens × 1 million docs = 3.2 billion dot products per query. Without approximation, late-interaction retrieval is a compute problem, not a search problem.

ruvnet

ruvector 2026: PDX Columnar Vector Layout — High-Performance Rust Vector Search

2–3.4× faster ANN scans with zero code changes and 100% recall, using LLVM auto-vectorisation via columnar memory layout (SIGMOD 2025)

ruvector now ships ruvector-pdx: the first Rust implementation of the PDX (Partition-Dimension-eXchange) data layout from CWI Amsterdam's SIGMOD 2025 paper. By transposing vector storage from row-major to columnar within each partition block, the inner L2 distance loop becomes stride-1 and LLVM auto-vectorises it with AVX2 — no hand-written intrinsics, no unsafe code, no platform-specific dependencies.

ruvnet

ruvector 2026: SOAR-IVF — High-Performance Rust Vector Search with Orthogonality-Amplified Residual Spilling

ruvector is a high-performance Rust vector database. This nightly research note introduces ruvector-soar: the first Rust implementation of SOAR-IVF (Spilling with Orthogonality-Amplified Residuals), the NeurIPS 2023 algorithm deployed in Google Cloud Vertex AI Vector Search. SOAR-IVF improves recall@10 by up to +10.4pp at the same query cost versus standard IVF-PQ — a critical improvement for production embedding search and RAG pipelines.

Keywords: approximate nearest neighbor search, IVF, vector search Rust,