CorTexManus: Decentralized Cognitive Specialized Architecture for Efficient, Real-Time Embodied AGI    

Abstract

CorTeXManus: Decentralized Cognitive Specialized Architecture for Efficient, Real-Time Embodied AGI Author: Khan Tahsin AbrarAffiliation: Independent Researcher, BangladeshORC-ID: https://orcid.org/0009-0009-4631-6768 Email: khan.tahsin.abrar.kta@gmail.comDate: 13 July, 2025Version: v1DOI: Pending Abstract:This paper proposes a biologically inspired AGI architecture where each cognitive function is hosted on its own specialized hardware module, forming a distributed system modeled after the human brain. By separating reasoning, vision, language, motor control, and memory into individual TexManus Units (TxMU), each powered by dedicated processors (GPU, NPU, LPU, TPU, etc.), the model achieves parallelism, energy efficiency, and real-time embodiment. Central coordination is handled by an ”AI Smart Router,” analogous to the human thalamus and corpus callosum, dynamically routing tasks based on input type and current system load. Unlike centralized Transformer-based LLMs, this architecture enables intelligent agents to process, respond, and adapt in a human-like way across both physical and virtual environments. This work presents a conceptual prototype utilizing open-source LLMs and vision models distributed across cloud (e.g., Colab, HuggingFace) and edge hardware (e.g., Jetson Nano, Coral). The proposed system offers a scalable, low-cost pathway toward embodied AGI and challenges the dominant paradigm of centralized neural monoliths.Keywords: AGI, modular AI, distributed cognition, neuromorphic architecture, robot brain, TxMU, AI router, hardware-software co-design, embodied intelligence, biologically inspired AIIntroduction: The field of artificial intelligence has made remarkable strides in recent years, especially with the rise of large language models (LLMs) such as GPT-4, Claude, and Gemini. These models showcase emergent reasoning abilities, multilingual fluency, and even basic multimodal integration. However, their architecture remains inherently monolithic, relying on centralized compute, enormous parameter counts, and singular model cores for all cognitive functions. This centralization leads to significant inefficiencies in energy usage, latency, cost, and system adaptability.In contrast, human cognition operates on a fundamentally different paradigm: the brain is a collection of specialized, distributed processing centers, each evolved to handle a specific function: vision, language, memory, emotion, planning, etc. These modules communicate through a vast neural network and function concurrently rather than sequentially. This anatomical and functional modularity allows for efficient parallel processing, adaptive learning, and embodied interaction with the environment.This paper introduces a novel AGI design that mirrors this biological model. We propose the concept of TexManus Units (TxMUs): modular, distributed AI components embedded with task-specific processing hardware such as GPUs, NPUs, LPUs, and TPUs. Coordinated by an intelligent central routing system, the AI Smart Router, these units collectively emulate the brain’s decentralized functionality. Each unit operates semi-independently yet cohesively, forming a synthetic cognitive network capable of complex, real-time interaction.We further propose physical embodiment of these modules in robotic systems, with each limb or sensor cluster containing localized compute for its assigned task. This structure enables more natural, low-latency interaction and scalable intelligence in both digital and physical agents. Our prototype and proposed implementation leverage existing cloud resources (Google Colab, HuggingFace) and edge AI hardware (Jetson Nano, Coral, Raspberry Pi with accelerators) to demonstrate feasibility and efficiency.By shifting from monolithic model architectures to modular neuro-inspired systems, this work lays a foundation for building robust, interpretable, and scalable AGI systems; not by increasing model size, but by mimicking the evolutionary bri