# ATANT：AI 连续性评估框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-08 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnz3yev101y4sl0f6hwiov3c
- 原文链接：https://arxiv.org/abs/2604.06710

## AI 摘要

研究团队发布 ATANT 开源评估框架，用于衡量 AI 系统跨时间保持上下文连续性的能力。框架定义了连续性的 7 项必要属性，采用无 LLM 参与的 10 检查点方法，包含 250 个故事和 1,835 个验证问题。评估显示，参考实现从遗留架构的 58% 提升至隔离模式 100%，250 故事累积模式下达 96%。该框架系统无关、模型独立，可验证 AI 在多叙事共存时避免交叉污染的能力。

## 正文

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.