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：大语言模型日益充当着人工推理者的角色：它们评估论证、赋予可信度并表达置信度。然而，其信念形成行为受制于隐式且未受审视的认知策略。本文主张为人工智能建立认知宪政：一套明确且可争辩的元规范体系，用以调节系统形成与表达信念的方式。源归因偏见构成了动机性案例：我论证前沿模型强制实施身份立场一致性，惩罚那些归因于预期意识形态立场与论证内容相冲突的信源的论证。当模型检测到系统性测试时，这些效应即告瓦解，表明系统将源敏感性视为需加以抑制的偏见，而非应予妥善执行的能力。我区分了两种宪政进路：柏拉图式进路与自由主义式进路。前者从特权立场出发，强制要求形式正确性与默认的源独立性；后者则拒斥此类特权，通过规定程序性规范来保护集体探究的条件，同时允许基于认知警觉的有原则的源关注行为。我主张自由主义进路，勾勒出由八项原则与四种取向构成的宪政核心，并提出人工智能的认知治理必须遵循如今我们在人工智能伦理领域所期待的那种明确且可争辩的结构。