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This release rethinks how `arrowspace` builds and queries graph structure from high‑dimensional embedding up to 10⁵ items and 10³ features.

The Laplacian computation now:
• condenses data with clustering and density‑aware sampling,
• projects dimensionality proportionally to the problem size (centroids) and keeps queries consistent with that projection, and
• sparsifies the graph with a fast spectral method to preserve structure while slashing cost.

`ArrowSpace` has evolved with three critical enhancements that improve both performance and analytical capabilities for high-dimensional data processing. These improvements address fundamental challenges in graph construction, data scaling, and computational efficiency—delivering measurable gains that matter to production systems

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.