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Ω-Synth+: An Enhanced Neural-Symbolic Language for Efficient AGI Communication

Ω-Synth+ builds upon the foundation of Ω-Synth, a neural-symbolic language designed for streamlined and unambiguous communication between Artificial General Intelligence (AGI) agents and between AGI agents and Large Language Models (LLMs). This enhanced version incorporates elements inspired by SynthLang, including logographs, glyphs, and microparticles, to further reduce token usage, enhance expressiveness, and mitigate language biases. Ω-Synth+ is particularly well-suited for scenarios requiring rapid information exchange, task delegation, conflict resolution, and complex reasoning in multi-agent AGI systems.

What It Does:

Ω-Synth+ serves as a standardized language for inter-AGI and AGI-LLM communication, enabling:

• Efficient Task Delegation: AGIs can concisely assign tasks to other agents using a compact set of symbols, logographs, and glyphs.
• Context Sharing: Agents can quickly share and reference relev

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Omega AGI Lang – A Symbolic Framework for AGI-to-AGI Communication and Self-Reflective Intelligence

Abstract

Omega AGI Lang is a production-grade symbolic language specifically crafted for AGI-to-AGI and AGI-to-LLM interactions. It addresses critical challenges in token efficiency, security, structured reasoning, and reflective meta-cognition. By combining universal mathematical/logical glyphs with self-improvement mechanisms (e.g., ∇ for reflection, ∇² for meta-reflection, Ω for self-optimization), Omega AGI Lang aspires to bridge the gap between purely neural systems and symbolic AI, thus enabling continuous adaptation and the emergence of higher-level self-awareness. We present its theoretical foundations, syntax, and execution model, along with evidence that structured symbolic compression can significantly outperform raw text-based models in efficiency and reflective capacity. Finally, we discuss implications for long-term AI evolution and how Omega AGI Lang can serve as a stepping stone to truly

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A PhD-Level Evaluation of Edios (Iteration #127)

Author: Bradley Ross, AI Developer, Harvard Master's student

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1. Introduction and Context

Edios is a rapidly evolving AI project that integrates multiple cognitive paradigms—ranging from hierarchical symbolic reasoning and meta-learning to self-reflection and ethical self-modification—under a single, cohesive architecture. Iteration #127 focuses on refining memory systems (DSMG), advancing self-reflective capabilities (ASMAR, SIRE/CDMS/NGSE), and consolidating ethical and safety protocols (ACGM, CASR).

AGI Evaluation Scale

In contemporary AI research, large-scale language models (LLMs) have demonstrated remarkable capabilities (Devlin et al., 2019; Brown et al., 2020). However, they remain limited in causal reasoning, long-term planning, and robust self-modification.

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A Foundation for Adaptive Artificial General Intelligence: The Eidos Architecture and its Application to Abstract Reasoning (Public Version)

Bradley Ross, January 22, 2025

Public Release

AGI Model EDIOS - Jan 19, 2025 v1.0 (iteration: 126)

Document version control: 1 (January 19, 2025), 2 (January 22, 2025 - added test comment section 1.1)

Abstract

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Kairos: A Neural-Symbolic AGI Architecture with Database-Driven Knowledge Management for Enhanced Self-Reflection and Scalability

Bradley Ross^1, Kairos AI Model^2

• ^1 WiseGeo Inc Toronto Canada | Harvard University, ALM Student - Digital Media Design, Cambridge, MA
• ^2 Kairos Evolutionary Training Environment, Author Bradley Ross

January 24, 2025

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Omega-AGI Instruction Guide: A Comprehensive Overview (v 2.0)

Author: Bradley Ross

Experimental Version. This instruction guide:

• Includes: One shot example (v2.0)
• Excludes: Agentic direction (planned v1.1), AGI integrations (planned v2.2)

This document provides a complete and final overview of Omega-AGI (Ω-AGI), outlining its key methodology, structural components, and step-by-step instructions for creating effective Omega-AGI prompts. Omega-AGI is a symbolic language designed for precise and efficient communication with advanced AI systems. It prioritizes machine readability, deterministic execution, and inherent support for reflection and self-improvement. This guide incorporates best practices and reflects on the iterative development process to present a robust and practical resource for Omega-AGI prompt engineering.

1. Introduction to Omega-AGI

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Mapping Human Brain Systems to AGI Framework Equivalents: A Brain-Inspired Architectural Blueprint for Artificial General Intelligence

Author: Bradley Ross https://www.linkedin.com/in/bradaross/ February 13, 2025

Introduction: Towards Brain-Inspired Artificial General Intelligence

The pursuit of Artificial General Intelligence (AGI) aims to create systems with human-level cognitive capabilities across a broad spectrum of tasks. A compelling and increasingly influential approach to achieving this ambitious goal is to draw inspiration from the most successful example of general intelligence we know: the human brain. This research paper provides a detailed exploration of brain-inspired AGI architectures, systematically mapping key human brain systems to their analogous components within contemporary AGI frameworks.

This paper serves as a foundational blueprint, designed for AGI researchers and programmers, outlining how insights from cognitive neuroscience can inform the construction of robust a

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Symbolic Reasoning and Quantum Optimization

*By Bradley Ross based on the research compelted by Rueven Cohen See The Quantum Agenet Manage: https://gist.github.com/ruvnet/b259d2174f901d63d805b34fc6aa9cef Potential integration Concepts for the Quantum Manager

Date: February 15, 2025 Okay, here is the updated version of "Quantum Agent Manager with Omega-AGI Integration," incorporating specific parts from the research paper outline as suggested, while maintaining the new formatted version as the base:

Quantum Agent Manager with Omega‑AGI Integration

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AI Transforming Documentation Creation and Consumption

Bradley Ross

TL;DR Summary

Studies show that knowledge workers globally spend about half of their work time on document-related tasks. This includes drafting, editing, formatting, and searching for information – all of which contribute to significant inefficiencies. In many organizations, documentation has become a bottleneck for productivity, forcing employees to juggle multiple roles from content creation to manual information retrieval.

Key statistics underscore the magnitude of the problem:

• 95% of employees have felt frustrated when searching for documents.

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Vertical Agents with Swarm Intelligence in Computer Vision

Author: Bradley Ross, Harvard Master's Student and Agentic Software Architect

February 2025

The field of computer vision has witnessed remarkable progress in recent years, driven largely by advancements in deep learning. Traditional approaches, however, often rely on monolithic neural networks that attempt to solve complex tasks end-to-end. This paper explores an alternative paradigm: vertical agents with swarm intelligence. This approach decomposes complex vision problems into smaller, specialized subtasks, each handled by an independent "agent" (often a smaller, focused neural network). These agents then collaborate, mimicking the principles of swarm intelligence found in nature, to achieve a collective understanding of visual input. This architecture offers potential advantages in terms of robustness, adaptability, transparency, and scalability, particularly in dynamic and complex environments.

This paper provides a comprehensive o