Foundation models have rapidly advanced AI, raising the question of whether their decisions will ultimately surpass human strategies in real-world domains. The exponential, and possibly super-exponential, pace of AI development makes such analysis elusive. Nevertheless, many application areas that matter for daily life and society show only modest gains so far; a prominent case is diagnosing and treating dynamically evolving disease in intensive care. The common challenge is adapting complex systems to dynamic environments. Effective strategies must optimize outcomes in systems composed of strongly interacting functions while avoiding shared side effects; this requires reliable, self-adaptive modeling. These tasks align with building digital twins of highly complex systems whose mechanisms are not fully or quantitatively understood. It is therefore essential to develop methods for self-adapting AI models with minimal data and limited mechanistic knowledge. As this challenge extends beyond medicine, AI should demonstrate clear superiority in these settings before assuming broader decision-making roles. We identify the curse of dimensionality as a fundamental barrier to efficient self-adaptation and argue that monolithic foundation models face conceptual limits in overcoming it. As an alternative, we propose a decentralized architecture of interacting small agent networks (SANs). We focus on agents representing the specialized substructure of the system, where each agent covers only a subset of the full system functions. Drawing on mathematical results on the learning behavior of SANs and evidence from existing applications, we argue that swarm-learning in diverse swarms can enable self-adaptive SANs to deliver superior decision-making in dynamic environments compared with monolithic foundation models, though at the cost of reduced reproducibility in detail.

翻译：基础模型已迅速推动人工智能发展，这引发了其决策是否最终将在现实领域超越人类策略的问题。人工智能以指数级甚至可能超指数级的速度发展，使得此类分析难以捉摸。然而，许多对日常生活和社会至关重要的应用领域迄今仅显示出有限的进展；一个突出的例子是重症监护中对动态演变疾病的诊断与治疗。共同的挑战在于使复杂系统适应动态环境。有效策略必须在由强相互作用功能组成的系统中优化结果，同时避免共同的副作用；这需要可靠的自适应建模。这些任务与构建高度复杂系统的数字孪生体相一致，这些系统的机制尚未被完全或定量理解。因此，开发具有最小数据和有限机制知识的自适应人工智能方法至关重要。由于这一挑战超越了医学领域，人工智能应在承担更广泛决策角色之前，在这些场景中展现出明确的优越性。我们将维度诅咒确定为高效自适应的基本障碍，并认为单一的基础模型在克服这一障碍时面临概念上的限制。作为替代方案，我们提出了一种由交互式小型代理网络（SANs）组成的去中心化架构。我们专注于代表系统专业化子结构的代理，每个代理仅覆盖完整系统功能的一个子集。基于对SANs学习行为的数学研究结果以及现有应用的证据，我们认为，多样化的群体学习能够使自适应SANs在动态环境中提供优于单一基础模型的决策能力，尽管代价是降低了细节上的可复现性。