👋 Welcome!

Notebook walkthrough: using Rayleigh energy as a landscape signal for high-dimensional embedding spaces

• Feature-space Laplacian and per-item spectral smoothness
• Augmenting KDE, Diffusion Maps and Basin-Hopping with arrowspace
• BasinHop + ArrowSpace achieves 100% cluster purity at α = 0.35
• Baseline for mechanistic interpretability of latent spaces

Testing all aspect of Graph Wiring on semantic data.

• Why geometry fails in retrieval
• Measuring the win: MRR‑Top0
• `arrowspace` can relevantly improve RAG systems

Testing all aspect of Graph Wiring on semantic data.

• How fast is `arrowspace`
• How accurate is `arrowspace`
• `arrowspace` can relevantly improve RAG systems

Domain memory injected directly inside self-attention via a persistent Graph Laplacian (distilled knowledge graphs with arrowspace).

• Replaces the dot-product attention kernel with a topology-aware scalar signal (taumode / λ-distance), so attention is driven by distances in a domain manifold rather than raw geometry.
• Targets scaling pain points: ~50% KV-cache savings (values + λ_k instead of K+V) and ~20% faster time-per-token vs a nanoGPT baseline in the reported benchmarks.

AI safety through topology‑aware, energy‑informed retrieval that separates stable facts from risky intuitions.

• Shows how geometry‑only vector search and semantic caching accumulate retrieval errors, turning context drift into subtle hallucinations.
• Introduces arrowspace as an “open search” layer where graph Laplacians, energy dispersion, and topology‑quality scores expose and constrain off‑manifold results instead of hiding them inside black‑box similarity.

Test‑bed milestone for a unified vector, graph, and key‑value engine built on spectral indexing and energy‑informed search.

• Turns any dataset into a features graph, enabling manifold‑aware search, matching, ranking, and dataset characterization at any lifecycle stage.
• Designed for high dimensions by default: robust on biotech‑scale sequences, large vocabularies, and model‑sized embedding spaces.

Deep Dive into a Rust implementation of a decoder-only transformer inspired by Karpathy's nanochat.

• Breaks down the architecture of a modern LLM, explaining the role of key components for an experienced audience.
• Covers modern techniques such as Rotary Position Embeddings (RoPE), Multi-Query Attention (MQA), RMSNorm, and the use of a Squared ReLU in the MLP.

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.