What question did this study set out to answer?

To develop an internal safety metric for memory-based autonomous systems that prioritizes continuity preservation.

March 26, 2026Open Access

The Alim-Continuity Index (Λ) and Silent Alarm Architecture for Identity-Preserving Autonomous Safety

Key Points

To develop an internal safety metric for memory-based autonomous systems that prioritizes continuity preservation.
Introduced the Alim-Continuity Index (Λ) as a measure of internal system integrity.
Defined safety in terms of continuity preservation instead of output correctness.
Developed a Silent Alarm Architecture to trigger safety mechanisms based on continuity thresholds.
Demonstrated the effectiveness of Λ in assessing internal safety of autonomous systems.
Showed that traditional output-based metrics are insufficient for ensuring system safety.
Proposed a framework that allows for interpretable monitoring without reliance on reward-based penalties.

Abstract

This work introduces the Alim-Continuity Index (Λ), a novel continuity-based internal safety metric for memory-bearing autonomous systems. Unlike conventional output-based evaluation, the proposed framework measures internal system integrity through the dynamic interaction of Temporary Memory (TM) and Bold Memory (BM). The model defines safety as continuity preservation rather than output correctness, governed by: Λ (t) = R (TM, BM) · e^- (ασ (t) + βΔφ (t) ) Here, R represents memory resonance, σ captures external disturbance, and Δφ represents internal continuity deficit. A Silent Alarm Architecture is proposed, where safety mechanisms are triggered when Λ falls below a critical threshold (Λc), without requiring simulated pain or reward-based penalties. This framework provides an identity-preserving approach to AI safety, offering interpretable and computationally efficient monitoring for autonomous systems and large language models.

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