Adaptive Resilience: Why AI Systems Must Remain Viable in Any Future
Year: 2026
Type: preprint
Abstract: Predictive optimization has reached structural limits as the design criterion for adaptive AI systems in high-consequence environments. Systems trained to maximize accuracy against expected future distributions fail under distribution shift, and the failure is not graceful. This paper argues that the next generation of adaptive AI orchestration must be designed for viability rather than for prediction accuracy — engineered to remain operational across futures that include the unforeseen, the rare, and the actively adversarial. The argument synthesizes three established lineages: Beer's Viable System Model, Hollnagel's resilience engineering, and Taleb's antifragility. The unifying principle: MILO does not predict the future. It remains viable in any future.
Author: Flores Montano, Jorge Enrique
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160821395
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