这篇论文从理论上解决了通用智能体部署时的可靠性问题，给出了具体的误差界限和认证方法，对智能体安全研究很有参考价值。
该论文证明通用智能体在大型场景下不可能具备万能能力，传统最坏情况分析无法区分关键瓶颈与无关失败。作者提出结构化认证框架，将受目标条件的性能映射到智能体内部世界模型的逐项保证。他们设计了基于深度组合目标过滤特定转换的算法，并证明在该目标下的通用智能体具有误差界为O(1/n)+O(δ)的结构化世界模型。该界限在δ较小的条件下是紧的，从而允许通过定位可靠的长时规划转换来认证部署通用智能体。
World Models in Pieces: Structural Certification for General Agents
In the big-world regime, agents cannot be universally capable and their ability is inevitably specialized across a world model in pieces. Consequently, standard uniform guarantees fail to distinguish between the understanding of critical bottlenecks and irrelevant failures. We first formalize this limitation by proving that general agents are not universal, rendering standard worst-case analysis uninformative. To overcome this, we introduce structural certification, a transition-local framework that maps bounded goal-conditioned performance to entry-wise guarantees on the agent's internal world model. Our main contribution is constructive. We provide algorithms that filter specific transitions using deep compositional goals and prove that a general agent on these goals has a structural world model with a $\mathcal{O}(1/n) + \mathcal{O}(δ)$ error bound. Conversely, this bound is tight in the small-$δ$ regime, whose existence is explicitly guaranteed by our certification. These results enable the certifiable deployment of general agents by localizing the specific transitions where long-horizon planning is reliable.