👋 Welcome!

Milestone release completes the search–matching–ranking pipeline with stabilized energymaps module, delivering spectral vector search that finds matches beyond geometric proximity.

• Two complete build paths: eigenmaps (spectral indexing from Laplacians) and energymaps (pure energy-first with optical compression, diffusion-split subcentroids, and automatic λτ computation).
• CVE corpus diffusion sweep (300K docs) achieves Avg MRR 0.75, NDCG@10 0.7239 (η=0.22, steps=8) with stable 75–83s build times, confirming negligible diffusion overhead and strong spectral ranking quality.

Rust implementation of DeepSeek-OCR compression achieves 10× token reduction, while ArrowSpace v0.18.0 introduces energy-informed retrieval that replaces cosine similarity with spectral graph properties.

• DeepEncoder architecture (SAM + CLIP + projector) replicated in Rust using burn.dev with cross-platform GPU support and five resolution modes from 64 to 400 tokens.
• Energy search with diffusion parameter sweep on CVE corpus achieves NDCG@10 ≈ 0.99 (η=0.05, steps=6) and MRR=1.0 (η=0.05, steps=4) without any cosine similarity.

Version 0.16.0 is out with quite relevant news and encouraging results for `arrowspace` to be one of the fastest approximate nearest neighbours algorithm available in the open.

Vector databases have become the backbone of modern AI workflows, particularly in RAG systems. But traditional approaches are fundamentally limited—they miss the deeper structural patterns that define how information relates within domains. Discover how ArrowSpace introduces energy-informed indexing through taumode, enabling AI systems with memory that truly understands domain contexts through spectral signatures and graph Laplacian energy.