ATANT: An Evaluation Framework for AI Continuity

Computer Science > Artificial Intelligence [Submitted on 8 Apr 2026] ATANT: An Evaluation Framework for AI Continuity Samuel Sameer Tanguturi 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 this https URL. The full 250-story corpus will be released incrementally. Comments: 7 pages, 8 tables. Framework and evaluation protocol available at this https URL Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) ACM classes: I.2.7; I.2.6 Cite as: arXiv:2604.06710 [cs.AI]   (or arXiv:2604.06710v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.06710 Focus to learn more Submission history From: Samuel Tanguturi [view email] [v1] Wed, 8 Apr 2026 06:04:51 UTC (12 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)