Organizational knowledge used by AI agents typically lacks epistemic structure: retrieval systems surface semantically relevant content without distinguishing binding decisions from abandoned hypotheses, contested claims from settled ones, or known facts from unresolved questions. We argue that the ceiling on organizational AI is not retrieval fidelity but \emph{epistemic} fidelity--the system's ability to represent commitment strength, contradiction status, and organizational ignorance as computable properties. We present OIDA, a framework that structures organizational knowledge as typed Knowledge Objects carrying epistemic class, importance scores with class-specific decay, and signed contradiction edges. The Knowledge Gravity Engine maintains scores deterministically with proved convergence guarantees (sufficient condition: max degree $< 7$; empirically robust to degree 43). OIDA introduces QUESTION-as-modeled-ignorance: a primitive with inverse decay that surfaces what an organization does \emph{not} know with increasing urgency--a mechanism absent from all surveyed systems. We describe the Epistemic Quality Score (EQS), a five-component evaluation methodology with explicit circularity analysis. In a controlled comparison ($n{=}10$ response pairs), OIDA's RAG condition (3,868 tokens) achieves EQS 0.530 vs.\ 0.848 for a full-context baseline (108,687 tokens); the $28.1\times$ token budget difference is the primary confound. The QUESTION mechanism is statistically validated (Fisher $p{=}0.0325$, OR$=21.0$). The formal properties are established; the decisive ablation at equal token budget (E4) is pre-registered and not yet run.

翻译：组织知识在被AI代理使用时，通常缺乏认知结构：检索系统能提取语义相关的内容，却无法区分已确定的决策与已放弃的假说、有争议的主张与已达成共识的结论、已知的事实与未解决的问题。我们认为组织AI的上限并非检索保真度，而是认知保真度——即系统将承诺强度、矛盾状态和组织无知表达为可计算属性的能力。我们提出OIDA框架，该框架将组织知识结构化为带类型知识对象，每个对象携带认知类别、具有类别特定衰减的重要性得分，以及带符号的矛盾边。知识引力引擎以确定性方式维护得分，并具有经过证明的收敛保证（充分条件：最大度数<7；经验上对度数为43的情况保持稳健）。OIDA将问题引入为建模后的无知：一种具有逆向衰减的原语，能以日益紧迫的方式呈现组织所不知道的内容——这一机制在所有被调查的系统中均缺失。我们描述了认知质量评分（EQS），这是一种包含五组分的评估方法论，并带有明确的循环性分析。在受控比较中（n=10个响应对），OIDA的RAG条件（3868个token）达到EQS 0.530，而全上下文基线（108687个token）为0.848；28.1倍的token预算差异是主要混淆因素。问题机制经统计验证（Fisher检验p=0.0325，OR=21.0）。其形式化性质已确立；在相等token预算下的决定性消融实验（E4）已预注册但尚未运行。