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Introduction

This document provides a comprehensive overview of five advanced algorithms, detailing their technical implementations using Python and Pydantic for data validation, as well as asynchronous programming for efficiency. Each algorithm is also explored in terms of practical applications across various domains. The algorithms covered include:

1. NEUMANN: Differentiable Logic Programs for Abstract Visual Reasoning - This algorithm integrates differentiable logic programming with neural networks, enabling advanced visual reasoning and logical deduction. It is particularly useful in computer vision, robotics, and medical imaging.

2. Scheduled Policy Optimization for Natural Language Communication - This algorithm optimizes policies for natural language communication, enhancing dialogue systems, customer support automation, and machine translation. It leverages policy gradient methods and scheduled learning to improve interaction quality and efficiency.

3. **LEFT: Logic-Enhanced Foundatio

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Reuven Cohen's Cognitive Framework for Logic, Reasoning, and Comprehension

1. Understanding the Query

• Step 1: Clarify the Question
  • Initial Interpretation: Break down the question into its core components. Identify the main topic, specific details, and expected outcome.
  • Restate the Query: Paraphrase the question internally to ensure clear understanding.
  • Focused Attention: Capture the essence of the query and avoid misinterpretation.

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AgentDB

A sub-millisecond memory engine built for autonomous agents

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Resonant-Interference Engine: Production Implementation Blueprint

I’ll create a simple, accessible introduction for you:

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Simple Introduction

Imagine dropping pebbles into a pond. The ripples spread, overlap, and create complex interference patterns. Now imagine those patterns could organize themselves into stable, meaningful structures—that’s the core idea behind this system.

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🔬 Research Swarm - Local AI Research Agent System

A fully local, SQLite-based AI research agent system with long-horizon recursive framework, AgentDB self-learning, and MCP server support.

Created by rUv | GitHub

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#!/usr/bin/env node

/**
 * Research Swarm MCP Server
 * Model Context Protocol server for research swarm tools
 * 
 * Supports both stdio and HTTP/SSE streaming transports
 * Based on agentic-flow MCP architecture
 */

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LangGraph Rust/WASM Implementation Specification

Production-Ready Port with AgentDB Integration

Built by: ruv.io
Version: 1.0.0
Target: 100% API compatibility with LangGraph Python
Date: November 11, 2025

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name	version	description	tags	author	created	updated
agent-goal-planner	1.0.0	Goal-Oriented Action Planning (GOAP) specialist that creates intelligent plans for complex objectives using gaming AI techniques	planning goap strategy ai objectives	Claude Code	2025-11-11	2025-11-11

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ROS3 COMPREHENSIVE TECHNICAL SPECIFICATION

Robot Operating System 3: Ground-Up Rewrite for the Next 20 Years

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EXECUTIVE SUMMARY

This comprehensive technical specification defines ROS3, a complete ground-up rewrite of ROS2 in Rust, distributed via npm with hybrid WASM/native deployment, featuring native AI agent integration and MCP protocol support. Based on extensive research across ROS2 architecture, Rust robotics ecosystem, WASM/NAPI hybrid strategies, agentic AI frameworks, MCP protocol, and next-generation robotics technologies, this specification provides concrete implementation guidance, performance targets, and integration patterns for a 20-year vision.

Key Innovations:

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Reverse engineering ICE’s AI to understand what’s really running under the hood.

What I found isn’t just data analytics—it’s an automated surveillance network built for precision at scale. The system draws from DMV databases, data brokers, phone metadata, facial recognition, and license plate readers. Together, these feeds form a unified view of movement and identity across most of the U.S. adult population.

The data isn’t just collected; it’s synthesized. ICE’s AI links records, learns patterns, and ranks potential targets by probability, not certainty. In technical terms, it operates as an entity resolution and pattern inference engine that keeps improving with every data refresh. Accuracy improves with density, but so do the stakes. One mismatched address or facial false positive can cascade into real consequences for someone who has no idea they’re even in the system.

What stands out most is how the technology has shifted enforcement from reactive to predictive. It no longer waits for an event—it f