ATANT: An Evaluation Framework for AI Continuity
| Authors | Samuel Sameer Tanguturi |
| Year | 2026 |
| HF Upvotes | 1 |
| arXiv | 2604.06710 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
Plain English
ATANT is an evaluation framework that measures whether AI systems can maintain and reconstruct meaningful context over time—a capability called 'continuity.' The paper identifies a gap in the industry: while companies have built memory components like RAG pipelines and vector databases, there's no formal way to test if these actually work. The authors define continuity through 7 required properties, create a 10-checkpoint evaluation methodology that doesn't require an LLM to run the tests, and build a test corpus of 250 stories with 1,835 verification questions across 6 life domains to benchmark systems.
Core Technical Contribution
The core innovation is formalizing 'continuity' as a measurable system property with specific, testable criteria—something previously treated informally in the industry. The authors designed a novel evaluation methodology that operates without an LLM in the evaluation loop itself, avoiding circular dependency problems where you'd use an LLM to judge whether an LLM maintains context properly. They created ATANT as an open framework with a substantial narrative corpus (250 stories, 1,835 questions across 6 domains) that lets engineers benchmark how well their memory and context-handling mechanisms actually work in practice, not just in theory.
How It Works
The framework takes as input narrative stories (the paper uses 250 stories spanning multiple life domains like relationships, career, health, etc.) and generates verification questions at 10 checkpoints distributed across each story. At each checkpoint, an AI system must answer questions that probe whether it has correctly persisted, updated, disambiguated, and reconstructed context from the earlier parts of the narrative—for example, questions about character relationships, past events, or contextual details that would only be knowable if the system maintained continuity. The system produces responses which are then evaluated against ground truth without requiring an LLM judge; instead, the framework uses structured verification (likely factual matching and consistency checks). The output is a continuity score that reflects how well the system maintained and reasoned about accumulated context across the 10 checkpoints, with breakdowns by domain and continuity property type.
Production Impact
For teams building conversational AI, retrieval-augmented generation (RAG) systems, or multi-turn dialogue agents, this framework provides concrete measurements of whether your memory and context infrastructure actually delivers continuity—moving beyond 'we use a vector database' to 'our system scores 87% on narrative continuity.' The 10-checkpoint methodology is lightweight and doesn't require training; you can run it against any system that produces text outputs, making it practical for continuous evaluation in CI/CD pipelines. The narrative corpus provides a reusable benchmark, reducing the cost of building your own evaluation datasets. However, adoption requires accepting that narratives may not fully represent all use cases (customer support, technical queries, etc.), and the framework's computational cost scales with story length and checkpoint density. Integration is straightforward since it's LLM-agnostic and doesn't require model modifications—you feed it prompts and parse outputs.
Limitations and When Not to Use This
The evaluation assumes that continuity can be fully captured through narrative understanding and verification questions, but this may not transfer to non-narrative domains like technical support, code generation, or transaction-heavy systems where context needs are different. The paper doesn't describe how the 250-story corpus was created or validated, so there's risk of dataset bias that could favor certain system architectures or language patterns over others. The framework requires ground truth annotations for all 1,835 questions, which demands significant human effort and introduces subjective judgment about what 'correct' context maintenance looks like—especially for ambiguous or open-ended scenarios. Finally, 'continuity' as defined here may be necessary but not sufficient; a system could score well on narrative continuity but fail at other critical properties like factual accuracy, privacy preservation, or handling out-of-distribution contexts.
Research Context
This work addresses a known gap in AI evaluation: while memory mechanisms (RAG, vector databases, long context windows) have proliferated, formal evaluation frameworks remain scarce. It builds on the tradition of benchmark-driven evaluation (like GLUE, SQuAD for QA, or MTEB for embeddings) but applies it to a less-studied dimension—temporal context persistence. The paper opens research direction on how to measure and improve continuity as a first-class system property, similar to how reliability, fairness, and robustness became focal points after frameworks were published. The 250-story corpus and 10-checkpoint methodology could become a standard reference (like MMLU or HellaSwag), enabling comparative measurement of memory and context architectures across different model sizes, RAG strategies, and prompt engineering techniques.
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