AI 中文总结
本文指出文档AI的无状态瓶颈，提出持久证据文档状态框架，通过状态性审计和纵向基准测试，以五个指标评估持久状态的收益与风险，强调跨文档、会话和时间的记忆机制是未来关键。
AI 中文摘要
现代文档人工智能能够阅读合同、提取字段、对表格进行推理，并将答案定位到页面区域，然后却忘记一切。第二天处理修订件时从头开始：不保留模式，不检测矛盾，不积累经验。这是一个结构性选择，而非规模失败：当前系统是无状态函数。我们称之为无状态瓶颈。该瓶颈超越了参数扩展、上下文扩展和检索增强：存储提供持久性，检索提供访问，但两者都不能将观察结果整合为改进未来处理的知识。本综述将基于证据的持久文档状态形式化为一个统一框架，规定了将多模态证据转化为持久、具有来源链接的状态所需的操作和不变量。我们引入了一项状态性审计，表明十个代表性基准，根据八项状态性标准编码，未测试跨会话状态演化，并推导出一个纵向基准测试框架，包含五个反事实指标：经验增益、成本效率、记忆损害、遗忘保真度、覆盖保持，以表征持久文档状态的收益、成本、风险和可治理性。文档人工智能缺乏将持久状态与文档原生结构、来源和时间有效性耦合的机制。文档人工智能的下一个时代将由系统跨文档、跨会话和跨时间保留的内容来定义。
英文摘要
Modern Document AI reads contracts, extracts fields, reasons over tables, and grounds answers to page regions, then forgets everything. Processing an amendment the next day begins from scratch: no schema retained, no contradiction detected, no experience carried forward. This is a structural choice, not a scale failure: current systems are stateless functions. We call this the statelessness bottleneck. This bottleneck lies beyond parameter scaling, context extension, and retrieval augmentation: storage provides persistence and retrieval provides access, but neither consolidates observations into knowledge that improves future processing. This survey formalizes persistent evidence-grounded document state as a unifying framework, specifying the operations and invariants required to convert multimodal evidence into durable, provenance-linked state. We introduce a statefulness audit showing that ten representative benchmarks, coded against eight statefulness criteria, leave cross-session state evolution untested, and derive a longitudinal benchmark harness with five counterfactual metrics: Experience Gain, Cost Efficiency, Memory Harm, Forgetting Fidelity, Coverage Retention, to characterize the benefit, cost, risk, and governability of persistent document state. Document AI lacks mechanisms coupling persistent state to document-native structure, provenance, and temporal validity. The next era of Document AI will be defined by what systems retain across documents, sessions, and time.
