CorTexManus: Decentralized Cognitive Specialized Architecture for Efficient, Real-Time Embodied AGI
Khan Tahsin Abrar · 2025
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: [email protected]: 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 brilliance of the human brain.Biological Basis and Brain Inspiration: The human brain is nature’s most advanced computational architecture. Unlike artificial neural networks that attempt to emulate its logic through weight matrices and backpropagation, the brain operates as a highly modular, massively parallel, and deeply embodied system. Each region of the brain performs specific, often specialized functions, with communication facilitated by intricate biochemical signaling and electrical conduction pathways.Key anatomical modules include the frontal lobe (executive functions, planning), occipital lobe (vision), temporal lobe (language and memory), parietal lobe (sensory processing and spatial reasoning), cerebellum (motor coordination), limbic system (emotion, motivation), and brainstem (autonomic control). These parts collaborate through neural fibers and centralized hubs like the thalamus (which routes sensory input) and the corpus callosum (which bridges the brain’s hemispheres).This modular structure is key to the brain’s adaptability, resilience, and efficiency. When one region is damaged, others can often compensate, a redundancy and flexibility rarely seen in artificial systems. Moreover, the brain’s localized computation reduces latency and power demands, as processes are handled near their sensory or motor origins rather than in a single central processor.Our proposed architecture emulates this design directly. Each TeXManus Unit (TxMU) corresponds to a brain lobe or system, with its own dedicated processor matching the cognitive role:Occipital Lobe → GPU for image and vision tasksTemporal Lobe → LPU/NPU for language, audio, and semantic reasoningFrontal Lobe → TPU/NPU for logic, planning, and decision-makingCerebellum → MCU or low-power NPU for motor skill executionLimbic System → Analog circuits + memory units for emotion modeling and value-weighted learningThalamus/Corpus Callosum → AI Smart Router for task routing and inter-TXMU coordinationBy mapping cognitive roles to matched silicon, we propose not just a brain-like architecture in theory but a directly implementable hardware-software co-design that fuses biological inspiration with engineering pragmatism. This section lays the neuroanatomical justification for the TxMU system, establishing the cognitive fidelity and potential efficiency of our modular AGI design.