MonaVec: A Training-Free Embedded Vector Search Kernel for Edge and Offline AI Systems

• URL: http://arxiv.org/abs/2606.19458v1
• Date: Wed, 17 Jun 2026 18:00:54 GMT
• Title: MonaVec: A Training-Free Embedded Vector Search Kernel for Edge and Offline AI Systems
• Abstract summary: MonaVec is a deterministic, embedded vector-search kernel for edge and offline AI.<n>It targets on-device RAG, offline embedded retrieval -- the niche occupies for relational data: one file, one call, runs anywhere.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present MonaVec, a deterministic, embedded vector-search kernel for edge and offline AI -- settings where server infrastructure, network connectivity, and training data are all unavailable. Existing vector-search systems assume a persistent server, gigabytes of RAM, or a training pass over the corpus; MonaVec instead targets the deployment profile of SQLite: one file, one function call, runs anywhere. Its quantization core is training-free by default and data-oblivious: a Randomized Hadamard Transform (RHDH) conditions any input distribution toward N(0,1), so precomputed Lloyd-Max tables quantize to 4 bits (8x smaller) with no learned codebook and no data pass. The index persists as a single .mvec file whose embedded ChaCha20 rotation seed makes results reproducible across architectures and byte-identical within a build -- a determinism guarantee that parallel-build graph libraries cannot offer. On semantic embeddings (AG News, 45K x 1024-dim BGE-M3, cosine), MonaVec 4-bit BruteForce reaches 0.960 Recall@10 in 27 MB -- leading float32 FAISS-IVF and 8-bit usearch on recall -- while trading peak throughput for byte-identical determinism. A single-pass global standardization (fit()) extends the same data-oblivious pipeline to magnitude-sensitive L2 data, and optional IvfFlat and HNSW backends carry it to million-vector corpora. MonaVec is implemented in pure Rust with Python bindings and runtime SIMD dispatch (AVX-512/AVX2/NEON/scalar). It targets on-device RAG, offline agents, and embedded retrieval -- the niche SQLite occupies for relational data: one file, one call, runs anywhere.

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