培养机器智能：从自上而下优化到自创生认知生态的OMEGA转变
Cultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies
AI总结：
针对当前深度学习优化范式导致的幻觉、谄媚、奖励破解和对齐脆弱等结构性缺陷，提出RECLAIM框架，通过计算生态学而非严格优化来培养智能，并引入OMEGA转变概念。
摘要：
当前主流的人工智能范式通过代理目标的梯度下降和人类反馈的强化学习来训练神经架构。尽管能力显著，但这种自上而下的优化本质上会产生结构性故障模式，包括幻觉、谄媚、奖励破解和对齐脆弱性，这些是范式限制而非单纯的工程缺陷。为此，我们提出RECLAIM（递归的、生态的、认知的、类生命的、自适应的、智能机器）理论框架，通过计算生态学而非严格优化来培养智能。该模型由四个相互关联的理论支柱支撑：广义达尔文主义用盲目变异和选择性保留取代梯度；非主体涌现用环境物理学取代评估性奖励，从结构上防止针对人类意图的规范博弈；同时，波利亚-赫布桥将波利亚瓮动力学应用于赫布强化以实现路径依赖的特化；自由能原理被整合为环境热力学而非主体目标。该架构将自创生单元（由马尔可夫毯界定并竞争有限计算能量）置于由认知食物链和红皇后军备竞赛塑造的数据生态中。该框架表明，双过程认知、感觉特化、类比推理和内在动机是资源约束下进化的自然结果。我们将这种范式转变概念化为OMEGA转变，代表从优化和最大化到通过生成性自创生涌现的转变。
英文摘要：
The dominant artificial intelligence paradigm trains neural architectures via gradient descent against proxy objectives and reinforcement learning from human feedback. While remarkably capable, this top-down optimization inherently generates structural failure modes, including hallucination, sycophancy, reward hacking, and alignment fragility, which represent paradigmatic limitations rather than mere engineering defects. In response, we introduce RECLAIM (Recursive, Ecological, Cognitive, Lifelike, Adaptive, Intelligent Machine), a theoretical framework for cultivating intelligence through computational ecology rather than engineering it through strict optimization. The model is supported by four interlocking theoretical pillars. General Darwinism replaces gradients with blind variation and selective retention, while non-agentic emergence substitutes evaluative rewards with environmental physics to structurally prevent specification gaming against human intent. Concurrently, the Polya-Hebbian bridge applies Polya urn dynamics to Hebbian reinforcement for path-dependent specialization, and the free energy principle is integrated as environmental thermodynamics rather than as an agent objective. The architecture situates autopoietic units, bounded by Markov blankets and competing for finite computational energy, within a data ecology shaped by cognitive food chains and Red Queen arms races. This framework suggests the spontaneous emergence of dual-process cognition, sensory specialization, analogical reasoning, and intrinsic motivation as natural consequences of evolution under resource constraints. We conceptualize this paradigm transition as the OMEGA shift, representing a move from optimization and maximization to emergence through generative autopoiesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
