AI intelligence is limited by the lack of an architecture that carries forward understanding across sessions, and the proposed continuity layer with Decomposed Trace Convergence Memory addresses this by enabling persistent state as a system property.
ATANT: An Evaluation Framework for AI Continuity
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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.
fields
cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Existing memory benchmarks cover at most two of the seven continuity properties from ATANT v1.0, with a median of one and none covering more than two.
citing papers explorer
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The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward
AI intelligence is limited by the lack of an architecture that carries forward understanding across sessions, and the proposed continuity layer with Decomposed Trace Convergence Memory addresses this by enabling persistent state as a system property.
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ATANT v1.1: Positioning Continuity Evaluation Against Memory, Long-Context, and Agentic-Memory Benchmarks
Existing memory benchmarks cover at most two of the seven continuity properties from ATANT v1.0, with a median of one and none covering more than two.