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arXiv · 2505.17696

Enhancing AI System Resiliency: Formulation and Guarantee for LSTM Resilience Based on Control Theory

Sota Yoshihara·Ryosuke Yamamoto·Hiroyuki Kusumoto·Masanari Shimura
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Abstract

This paper proposes a novel theoretical framework for guaranteeing and evaluating the resilience of long short-term memory (LSTM) networks in control systems. We introduce "recovery time" as a new metric of resilience in order to quantify the time required for an LSTM to return to its normal state after anomalous inputs. By mathematically refining incremental input-to-state stability ($\delta$ISS) theory for LSTM, we derive a practical data-independent upper bound on recovery time. This upper bound gives us resilience-aware training. Experimental validation on simple models demonstrates the effectiveness of our resilience estimation and control methods, enhancing a foundation for rigorous quality assurance in safety-critical AI applications.

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Sota Yoshihara, Ryosuke Yamamoto, Hiroyuki Kusumoto, Masanari Shimura. 2025-05-23. Enhancing AI System Resiliency: Formulation and Guarantee for LSTM Resilience Based on Control Theory. https://arxiv.org/abs/2505.17696

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