What question did this study set out to answer?

The study aims to develop a metric for assessing the continuity of memory in autonomous AI systems.

May 3, 2026Open Access

The Alim–Continuity Index (Λ): A Runtime Continuity Metric and Silent Alarm Architecture for Memory-Bearing Autonomous AI Systems

Key Points

The study aims to develop a metric for assessing the continuity of memory in autonomous AI systems.
Developed the Alim-Continuity Index (ACI) to compute continuity metrics for AI systems.
Used resonance between temporal and bold memory anchors for monitoring semantic coherence.
Included simulation-stage prototype resources for testing and demonstration.
Introduced the formula Λ(t) = R(TM,BM) · exp(−(ασ(t) + βΔφ(t))) to evaluate memory continuity.
Demonstrated the importance of threshold calibration for practical application in AI systems.

Abstract

This Zenodo record archives the Version 2 submission package and demonstration resources for the Alim–Continuity Index (ACI), a simulation-stage runtime continuity metric and Silent Alarm architecture for memory-bearing autonomous AI systems. ACI computes Λ(t) = R(TM,BM) · exp(−(ασ(t) + βΔφ(t))) to monitor semantic coherence using resonance between Temporal Memory and Bold Memory anchors, local instability, and angular drift. The package includes the main manuscript, supplementary material, reproducibility notebook, and an interactive HTML prototype. This work is a research-stage prototype only. It is not safety-certified, not production-ready, and not validated for real autonomous vehicles, drones, robots, or space systems. It does not claim machine consciousness, genuine emotion, sentience, or subjective experience. Thresholds require per-model and per-domain calibration before any practical use.

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