We present ATANT (Automated Test for Acceptance of Narrative Truth), an open evaluation framework for measuring continuity in AI systems: the ability to persist, update, disambiguate, and reconstruct meaningful context across time. While the AI industry has produced memory components (RAG pipelines, vector databases, long context windows, profile layers), no published framework formally defines or measures whether these components produce genuine continuity. We define continuity as a system property with 7 required properties, introduce a 10-checkpoint evaluation methodology that operates without an LLM in the evaluation loop, and present a narrative test corpus of 250 stories comprising 1,835 verification questions across 6 life domains. We evaluate a reference implementation across 5 test suite iterations, progressing from 58% (legacy architecture) to 100% in isolated mode (250 stories) and 100% in 50-story cumulative mode, with 96% at 250-story cumulative scale. The cumulative result is the primary measure: when 250 distinct life narratives coexist in the same database, the system must retrieve the correct fact for the correct context without cross-contamination. ATANT is system-agnostic, model-independent, and designed as a sequenced methodology for building and validating continuity systems. The framework specification, example stories, and evaluation protocol are available at https://github.com/Kenotic-Labs/ATANT. The full 250-story corpus will be released incrementally.

翻译：我们提出ATANT（叙事真值接受自动化测试），这是一个用于衡量AI系统连续性的开放式评估框架，即系统在时间维度上持久化、更新、消歧并重构有意义语境的能力。尽管AI行业已开发出记忆组件（如RAG流水线、向量数据库、长上下文窗口、用户画像层），但尚无已发表的框架对上述组件是否产生真正连续性进行正式定义或评估。我们将连续性定义为具有7项必需属性的系统特性，引入无需LLM参与评估循环的10检查点评估方法，并发布包含6个生活领域、250个故事及1，835个验证问题的叙事测试语料库。我们通过5轮测试套件迭代评估参考实现，其性能从（传统架构）58%提升至隔离模式100%（250个故事）、累积模式100%（50个故事），在250个故事累积规模下达到96%。累积结果作为核心指标：当250个独立生活叙事共存于同一数据库时，系统须在正确语境中检索正确事实且避免交叉污染。ATANT具有系统无关性与模型无关性，旨在作为构建与验证连续性系统的序列化方法论。框架规范、示例故事及评估协议详见https://github.com/Kenotic-Labs/ATANT，完整250故事语料库将分阶段发布。