发表机构
Lyon 1 University; CNRS; Liris; IUF（里昂第一大学; 法国国家科学研究中心; 里昂信息学研究实验室; 法国大学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对AI生态系统决策混杂问题，提出构建共享可查询的因果世界系统（CWS），为面向决策与智能体AI的因果数据管理生态系统提供支持。
AI 中文摘要
现代AI不再是单一模型，而是一个生态系统：经典机器学习预测器、深度与多模态模型、大语言模型以及智能体，每个都在不同数据源上进行训练与调优，产生的大规模输出会成为其他组件的输入。运营该生态系统本质上是数据集成问题——其依赖的知识分散在数十个异构、独立管理的数据源中，必须协调并持续维护。但仅集成还不够：这些系统的预测受诸多相互作用因素影响，驱动结果的事件、决策与变量常与仅伴随结果的变量纠缠，若将此类相关性信号作为行动基础，会导致决策混杂。当智能体自主行动时，这一问题尤为突出：要实现可信可靠，智能体必须预判行动后果，而非仅从过往共现情况外推。因果推理可弥合这一差距，区分结果的驱动因素与相关因素，并能对生态系统数据开展规定性与反事实分析。因此，我们认为集成后的生态系统需要明确的因果层，提议将其构建为共享、持久、可查询的因果世界系统（Causal World System，CWS）。
英文摘要
Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others. Operating such an ecosystem is fundamentally a data integration problem - the knowledge it depends on is fragmented across dozens of heterogeneous, independently governed sources that must be reconciled and continually maintained. Yet integration alone is not enough. The predictions these systems make are shaped by many interacting factors, and the events, decisions, and variables that drive an outcome are routinely entangled with the ones that merely accompany it; treated as a basis for action, such correlational signals invite confounded decisions. This becomes acute once agents act autonomously: to be trustworthy and reliable, an agent must anticipate the consequences of its actions, not merely extrapolate from what has co-occurred before. Causal reasoning is what closes this gap, distinguishing the drivers of an outcome from its correlates, and enabling prescriptive and counterfactual analysis over the ecosystem's data. We therefore argue that the integrated ecosystem needs an explicit causal layer, and we propose to build it as a shared, persistent, queryable Causal World System (CWS).
CommentsAccepted at ACM AI Leadership Summit 2026
