Constraint Memory and Long-Horizon Failure in Artificial Intelligence: Why Alignment Decays Even When Local Behavior Succeeds

This paper presents a structural account of long-horizon failure in artificial intelligence systems. While AI systems often exhibit stable and acceptable behavior in short interactions, they frequently degrade over extended time horizons. These failures are typically attributed to distribution shift, objective mis-specification, or memory limitations. This work argues that such explanations are insufficient. Building on a constraint-first framework, the paper introduces the concept of constraint memory: the persistence of exclusion across time. It shows that systems may locally satisfy constraints without preserving them, leading to decay, drift, and recurrence of previously suppressed behaviors. Long-horizon failure is therefore not a failure of learning or recall, but a failure of constraint persistence. A formal framework is introduced using time-dependent constraint-admissible sets, demonstrating how admissible behaviors expand when constraints are not preserved. The analysis distinguishes constraint persistence from robustness and shows that reducing error probability is not equivalent to enforcing impossibility. This work generalizes across artificial and biological systems and is part of the YU-AI series.