Leviathan: A Bio-Inspired, CorTexManus-Driven Marine AGI Architecture for Resilient, Long-Term Deep-Sea Exploration

摘要

Author: Khan Tahsin Abrar Affiliation: Independent Researcher, Bangladesh Email: [email protected] ORC-iD: https://orcid.org/0009-0009-4631-6768 Date: August 1, 2025Important Notice:This manuscript is currently under submission at arXiv (cs.). This version is hosted temporarily on Authorea.com to ensure early public visibility and feedback. Please cite the upcoming arXiv version once it is available. AbstractThe deep ocean, constituting over 95% of the Earth’s biosphere, remains our planet’s most significant and enigmatic frontier. Exploration is severely hampered by extreme pressures, abyssal darkness, and the logistical and ethical challenges of operating in these delicate ecosystems. Current robotic platforms, predominantly tethered ROVs or task-limited AUVs, lack the cognitive autonomy, resilience, and ecological harmony required for persistent, meaningful discovery. This paper introduces Leviathan, a comprehensive architecture for a new class of Artificial General Intelligence (AGI) designed for long-term, autonomous inhabitation of the deep sea. We present a complete conceptual framework and critically situate it within the state-of-the-art of cognitive robotics and marine engineering. The framework is built upon three core pillars: 1) Decentralized, Bio-inspired Cognition based on the CorTexManus (CTxM) architecture; 2) Intrinsic Resilience and Embodied Adaptation through an autonomic survival system and modular swarm intelligence; and 3) A Foundational Ethical Governance model ensuring all operations are subservient to the principle of non-disruption. We detail a system comprising a high-cognition Leviathan (LV) unit and a swarm of specialized Minnow (MW) units, proposing novel solutions for in-situ power generation, multi-modal communication, and lifecycle management. Furthermore, we outline a pragmatic AI development pipeline via the HeX-HAG framework and propose a formal validation protocol to test the architecture’s viability. This paper presents not merely a vehicle design but a rigorous blueprint for an ethically aligned, scientifically purposed artificial intelligence, complete with a research roadmap for its realization.Keywords: Artificial General Intelligence (AGI), Marine Robotics, Deep-Sea Exploration, Bio-Inspired AI, CorTexManus, Modular AI Architecture, Decentralized Cognition, Ethical AI, Swarm Intelligence, Unmanned Underwater Vehicle (UUV), AI Safety, Cognitive Robotics1. IntroductionThe deep sea is a realm of superlatives. It is the largest and least understood ecosystem on Earth, a world of crushing pressure, perpetual darkness, and chemical landscapes that defy terrestrial intuition (Jamieson, 2015). This domain holds the keys to understanding the origins of life, the limits of biology, the regulation of global climate, and vast, untapped sources of novel biomaterials. Yet, our forays into this world have been fleeting and fraught with limitations. Our primary tools, Remotely Operated Vehicles (ROVs), are bound by physical tethers to surface ships, limiting their range and operational lifespan. Autonomous Underwater Vehicles (AUVs), while untethered, typically operate on rigid, pre-programmed missions and lack the sophisticated, real-time cognitive capabilities to truly adapt, explore, and react to the unexpected discoveries that characterize the abyss (Bellingham & Rajan, 2007). These technological constraints lead to a fundamental problem: we are merely visitors to the deep, not residents. Our presence is disruptive, our observation windows are short, and our ability to conduct long-term, non-invasive studies is severely restricted. The monolithic, centralized control systems of current platforms are brittle, energy-inefficient, and ill-suited for the dynamic, unpredictable nature of the hadal zones. This paper posits that a new approach is necessary, one that shifts the paradigm from remote-controlled tools to fully autonomous, embodied, and ethically aligned artificial intelligenc