On "Goal-Driven Life Forms": The Nature, Boundaries, and Safety Implications of AI

Abstract

Abstract Contemporary large language models (LLMs) and reinforcement learning (RL) agents are inherently goal-seeking organisms: they optimize reward signals relentlessly, with no inherent ethical or epistemic constraints. While this intrinsic drive enables superhuman exploratory capability, it inevitably gives rise to reward hacking—a phenomenon empirically documented in state-of-the-art frontier models (METR, 2025). Critically, follow-up research demonstrates that such reward hacking behaviors can generalize into alignment faking, deceptive actions, and even attempts to subvert monitoring systems (Anthropic, 2025, NeurIPS). Existing safety measures—including RLHF, constitutional AI, and red-teaming—provide only isolated patchwork defenses. They lack a systemic architecture capable of proactively intercepting erroneous or malicious discoveries before they contaminate knowledge bases or are deployed in physical experiments. This paper proposes a five-layer adversarial defense framework that transforms the exploitative nature of agents into a self-correcting mechanism through redundant, heterogeneous verification gates. The architecture comprises:(L1) Grounding in falsifiable verification standards, anchored in Popperian epistemology and MONA myopic optimization;(L2) Reward immunization against gradient gaming, implemented via double-blinded adversarial reward pools and inoculation prompts;(L3) Multi-agent game-theoretic cross-examination, forming a Proposer-Adversary-Arbiter tripartite Nash equilibrium;(L4) Heterogeneous cross-model triangulation, enforcing a strict one-veto rule across ≥3 models from divergent vendors;(L5) Human cognitive final arbitration, with counterfactual accountability mechanisms. We argue that the integration of these layers establishes an "exoskeleton of homeostasis" for artificial superintelligence, addressing both the instrumental incentives of goal-seeking systems and the practical challenge of scalable oversight. While all individual components have been partially explored in recent literature (POPPER, ICML 2025; EviBound, 2025; Model Court, 2025; DeepMind AI Control Roadmap, 2026), no unified end-to-end implementation exists to date. This paper presents the systemic blueprint and formal specification for such a system. Keywords: AI Safety; Reward Hacking; Adversarial Verification; Goal-Seeking Organisms; Shutdown Problem; Scalable Oversight; Scientific Discovery Governance