Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings, compositional shifts, and open-ended task variation -- differ in kind from the settings that have shaped prior OOD research, and are further complicated because the pretraining and post-training distributions of modern FMs are often only partially observed. Our position is that OOD for foundation models is a structurally distinct problem that cannot be solved within the prevailing model-centric paradigm, and that agentic systems constitute the missing paradigm required to address it. We defend this claim through four steps. First, we give a stage-aware formalization of OOD that accommodates partially observed multi-stage training distributions. Second, we prove a parameter coverage ceiling: there exist practically relevant inputs that no model-centric method (training-time or test-time) can handle within tolerance $\varepsilon$, for reasons intrinsic to parameter-based representation. Third, we characterize agentic OOD systems by four structural properties -- perception, strategy selection, external action, and closed-loop verification -- and show that they strictly extend the reachable set beyond the ceiling. Fourth, we respond to seven counterarguments, conceding two, and outline a research agenda. We do not claim that agentic methods subsume model-centric ones; we argue that the two are complementary, and that progress on FM-OOD requires explicit recognition of the agentic paradigm as a first-class research direction.

翻译：基础模型（FMs）正日益部署于开放世界场景中，其中分布偏移是常态而非例外。它们面临的分布外（OOD）现象——知识边界、能力上限、组成性偏移及开放式任务变化——在本质上不同于以往OOD研究所设定的场景，且因现代FM的预训练与后训练分布往往仅被部分观测而进一步复杂化。我们的立场是：基础模型的OOD是结构上独特的问题，无法在现有的以模型为中心的范式内解决，而智能体系统正是解决该问题所缺失的范式。我们通过四个步骤论证这一主张。首先，我们给出一个适应阶段感知的OOD形式化框架，能够容纳部分观测的多阶段训练分布。其次，我们证明参数覆盖上限：存在实际相关的输入，任何以模型为中心的方法（训练时或测试时）都无法在容忍度$\varepsilon$内处理，原因由参数化表征的内在特性决定。第三，我们通过四个结构性质——感知、策略选择、外部动作与闭环验证——刻画智能体OOD系统，并证明其严格扩展了可达集合以超越上述上限。第四，我们回应七项反驳论点（其中两项被接纳），并勾勒出研究议程。我们不主张智能体方法取代以模型为中心的方法；我们论证两者互为补充，且FM-OOD的进展需要明确承认智能体范式作为一流研究方向。