Artificial Intelligence (AI) has made remarkable strides in recent years, achieving feats of perception, reasoning, and decision-making once reserved for human cognition. However, even as AI systems gain sophistication, they remain fundamentally brittle when encountering internal failures, such as reasoning inconsistencies, model drift, or feedback anomalies. Traditional fault-tolerance mechanisms focus on hardware redundancy, software resets, or external human intervention, which are unsuitable for autonomous systems operating in remote or high-stakes environments.This paper proposes Self-HealAI, a conceptual framework for cognitive self-repair in AI architectures. Inspired by biological systems—such as neuroplasticity, immune responses, and resilience engineering—Self-HealAI integrates introspective diagnostics, adaptive reasoning layers, sandboxed repair environments, and ethically bounded governance modules. The goal is to transform resilience from a passive characteristic into an active, emergent capability, empowering AI agents to maintain cognitive integrity, correct reasoning faults, and evolve repair strategies over time.While purely theoretical, this framework aims to catalyze new research directions and guide the design of next-generation AI systems that can operate robustly in dynamic, unpredictable, and inaccessible contexts, from space exploration to critical infrastructure and autonomous healthcare.

https://www.preprints.org/manuscript/202506.0063/v1