rUv ruvnet

ADR-051: Infinite Context via Compaction-to-Memory Bridge

Status: Proposed Date: 2026-02-10 Authors: RuvNet, Claude Flow Team Version: 1.0.0 Related: ADR-006 (Unified Memory), ADR-009 (Hybrid Memory Backend), ADR-027 (RuVector PostgreSQL), ADR-048 (Auto Memory Integration), ADR-049 (Self-Learning Memory GNN)

Context

Architectural Comparison: Claude Flow V3 vs Claude Code TeammateTool

Date: 2026-01-25 Analysis: Side-by-side comparison of Claude Flow V3 swarm architecture (developed by rUv) and Claude Code's TeammateTool (discovered in v2.1.19)

Executive Summary

A detailed analysis reveals striking architectural similarities between Claude Flow V3's swarm system and Claude Code's TeammateTool. The terminology differs, but the core concepts, data structures, and workflows are nearly identical.

Coherence engine built on ruvector

Most systems try to get smarter by making better guesses. I am taking a different route. I want systems that stay stable under uncertainty by proving when the world still fits together and when it does not. That is the point of using a single underlying coherence object inside ruvector. Once the math is fixed, everything else becomes interpretation. Nodes can be facts, trades, vitals, devices, hypotheses, or internal beliefs. Edges become constraints: citations, causality, physiology, policy, physics. The same residual becomes contradiction energy, and the same gate becomes a refusal mechanism with a witness.

This creates a clean spectrum of applications without rewriting the core. Today it ships as anti hallucination guards for agents, market regime change throttles, and audit ready compliance proofs. Next it becomes safety first autonomy for drones, medical monitoring that escalates only on sustained disagreement, and zero trust security that detects structural incohe

RuVector WebAssembly Competitive Intelligence + Business Simulation Tutorial (rVite)

I’ve put together a new tutorial for RV Lite and RuVector that reflects how I actually work. Prediction by itself is noise. Knowing what might happen is useless if you cannot adapt, respond, and steer toward the outcome you want.

This system is about doing all three. It does not stop at forecasting a future state. It models pressure, uncertainty, and momentum, then plots a viable course forward and keeps adjusting that course as reality pushes back. Signals change, competitors move, assumptions break. The system notices, recalibrates, and guides the next step.

What makes this different is where and how it runs. RV Lite and RuVector operate directly in the browser using WebAssembly. That means fast feedback, privacy by default, and continuous learning without shipping your strategy to a server. Attention mechanisms surface what matters now. Graph and GNN structures capture how competitors influence each other. Simulations

Advanced Mathematics for Next-Gen Vector Search

Making ruvector Smarter: Four Game-Changing Algorithms

This guide explains four powerful mathematical techniques that will differentiate ruvector from every other vector database on the market. Each solves a real problem that current databases can’t handle well.

1. Optimal Transport: “Earth Mover’s Distance”

Designing a Rust-Based Long-Term Memory System (MIRAS + RuVector)

Building a long-term memory system in Rust that integrates Google’s MIRAS framework (Memory as an Optimization Problem) with the principles of RuVector requires combining theoretical insights with practical, high-performance components. The goal is a memory module that learns and updates at inference-time, storing important information (“surprises”) while pruning the rest, much like Google’s Titans architecture . We outline a modular design with core components for surprise-gated memory writes, retention/forgetting policies, associative memory updates, fast vector similarity search, and continuous embedding updates. We also suggest Rust crates (e.g. RuVector) that align with geometric memory, structured coherence, and update-on-inference principles.

Memory Write Gate (Surprise-Triggered Updates)

A surprise-based write gate decides when new information merits permanent storage. In Titans (which implements MIRAS), a “surprise metric” measur

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.

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	PowerInfer-Style Activation Locality Inference Engine for Ruvector (SPARC Specification)

	Specification

	Goals and Motivation: The goal is to create a high-speed inference engine that exploits the activation locality in neural networks (especially transformers) to accelerate on-device inference while preserving accuracy. Modern large models exhibit a power-law distribution of neuron activations – a small subset of “hot” neurons are consistently high-activation across inputs, while the majority are “cold” and only occasionally activate . By focusing compute on the most active neurons and skipping or offloading the rest, we can dramatically reduce effective model size and latency. The engine will leverage this insight (as in PowerInfer ) to meet edge deployment constraints. Key performance targets include multi-fold speedups (2×–10×) over dense inference and significant memory savings (e.g. 40%+ lower RAM usage ) with minimal accuracy impact (<1% drop on benchmarks ). It should enable running larger models