rUv ruvnet

Bio-Inspired Neural Computing: 20 Breakthrough Architectures for RuVector and Cognitum

Recent advances in computational neuroscience and neuromorphic engineering reveal 20 transformative opportunities for implementing brain-inspired algorithms in Rust-based systems. These span practical near-term implementations achieving sub-millisecond latency with 100-1000× energy improvements, to exotic approaches promising exponential capacity scaling. For RuVector’s vector database and Cognitum’s 256-core neural processors, the most impactful advances center on sparse distributed representations, three-factor local learning rules, and event-driven temporal processing—enabling online learning without catastrophic forgetting while maintaining edge-viable power budgets.

Sensing Layer: Input Processing and Feature Extraction

1. Event-Driven Sparse Coding with Dynamic Vision Sensors

RuVector MinCut

Continuous structural integrity as a first-class signal for systems that must not drift.

🧠 @ruvector/sona Integration Guide

Date: 2025-12-03 Status: ✅ READY FOR INTEGRATION Priority: HIGH Package: @ruvector/sona@0.1.1

📊 Executive Summary

🎉 E2B Agent Testing & Optimization - COMPLETE SUMMARY

Date: 2025-12-03 Status: ✅ ALL TESTING COMPLETE Agents Tested: 66+ agents across 5 categories Total Tests: 150+ comprehensive test scenarios Success Rate: 95%+ across all categories

Treat LFM2 as the reasoning head, ruvector as the world model and memory, and FastGRNN as the control circuit that decides how to use both.

LFM2 as the language core (700M and 1.2B, optionally 2.6B). ([liquid.ai][1])
ruvector as a vector plus graph memory with attention over neighborhoods.
FastGRNN as the tiny router RNN that decides how to use LFM2 and ruvector per request. ([arXiv][2])

You can adapt the language and infra stack (Python, Rust, Node) without changing the logic.

Latent Space Exploration: RuVector GNN Performance Breakthrough

TL;DR: We validated that RuVector with Graph Neural Networks achieves 8.2x faster vector search than industry baselines while using 18% less memory, with self-organizing capabilities that prevent 98% of performance degradation over time. This makes AgentDB v2 the first production-ready vector database with native AI learning.

🎯 What We Discovered (In Plain English)

The Big Picture

GNN Attention Mechanisms for Vector Search: Comprehensive Research Analysis

Research Report Date: November 28, 2025 Researcher: AgentDB Research Team Focus: Graph Neural Network (GNN) attention mechanisms in vector search, query enhancement, and information retrieval

Executive Summary

ruvector GNN Specification v0.1.0

Introduction

What is ruvector?

ruvector represents a fundamental shift in how we think about vector databases. Traditional systems treat the index as passive storage - you insert vectors, query them, get results. ruvector eliminates this separation entirely. The index itself becomes a neural network. Every query is a forward pass. Every insertion reshapes the learned topology. The database doesn’t just store embeddings - it reasons over them.

This convergence emerges from a simple observation: the HNSW algorithm, which powers most modern vector search, already constructs a navigable small-world graph. That graph structure is mathematically equivalent to sparse attention. By adding learnable edge weights and message-passing layers, we transform a static index into a living neural architecture that improves with use.

	{
	"version": 1,
	"saved_at": 1765475580767,
	"vectors": {
	"entries": [
	{
	"id": "doc_4",
	"vector": [
	0.4369376003742218,
	0.8703458905220032,

	High-Dimensional Universe Simulation Kernel in Rust

	This section provides a comprehensive Rust-style implementation of a simulation where "entities" (points) evolve on a dynamic submanifold embedded in a high-dimensional space. Each entity is represented by a high-dimensional state vector whose first 4 components are spacetime coordinates (time t and spatial coordinates x, y, z), and the remaining components are latent state variables (e.g. energy, mass, and other properties). We enforce that these state vectors lie on a specific manifold (such as a fixed-radius hypersphere or a Minkowski spacetime surface) via a projection step after each update. The update rule uses nearest neighbors with a Minkowski-like causal filter to ensure influences respect light-cone causality (no superluminal interaction
	agemozphysics.com
	). We also focus on performance by reusing allocations, aligning data to vector register boundaries, and supporting both single and double precision.

	Data Structures and Parameters

	We define a