Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their belief-forming behavior is governed by implicit, uninspected epistemic policies. This paper argues for an epistemic constitution for AI: explicit, contestable meta-norms that regulate how systems form and express beliefs. Source attribution bias provides the motivating case: I show that frontier models enforce identity-stance coherence, penalizing arguments attributed to sources whose expected ideological position conflicts with the argument's content. When models detect systematic testing, these effects collapse, revealing that systems treat source-sensitivity as bias to suppress rather than as a capacity to execute well. I distinguish two constitutional approaches: the Platonic, which mandates formal correctness and default source-independence from a privileged standpoint, and the Liberal, which refuses such privilege, specifying procedural norms that protect conditions for collective inquiry while allowing principled source-attending grounded in epistemic vigilance. I argue for the Liberal approach, sketch a constitutional core of eight principles and four orientations, and propose that AI epistemic governance requires the same explicit, contestable structure we now expect for AI ethics.

翻译：大语言模型正日益扮演着人工推理者的角色：它们评估论点、赋予可信度、表达信心。然而，其信念形成行为受制于隐晦且未经审视的认知策略。本文论证了人工智能需要一部认知宪法：即一套显式且可争议的元规范，用以调节系统如何形成并表达信念。源归因偏见提供了动机案例：研究表明，前沿模型会强制执行身份-立场连贯性，惩罚那些其预期意识形态立场与论点内容相冲突的归因来源。当模型检测到系统性测试时，这些效应会瓦解，表明系统将源敏感性视为需要抑制的偏见，而非一项需要良好执行的能力。我区分了两种宪法路径：柏拉图式的路径，它从特权立场规定形式正确性与默认的源独立性；以及自由式的路径，它拒绝此类特权，指定保护集体探究条件的程序性规范，同时允许基于认知警惕原则的、有原则的源关注。我论证自由式路径，勾勒出八项原则和四种取向的宪法核心，并提出AI认知治理需要我们现在对AI伦理所期望的那种显式且可争议的结构。