ATANT: An Evaluation Framework for AI Continuity

TL;DR

ATANT is an open evaluation framework that measures the ability of AI systems to maintain and manage meaningful context over time, ensuring genuine continuity across various components.

Contribution

It introduces a formal definition of continuity, a 10-checkpoint evaluation methodology, and a narrative test corpus for assessing AI memory and context management.

Findings

Evaluation shows improvement from 58% to 100% in isolated mode.

Cumulative mode achieves 96% accuracy with 250 stories.

Framework is system-agnostic and model-independent.

Abstract

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.