Constraint-Layered Drift Detection in Long-Context Language Model Evaluation

Thesis:This paper introduces a constraint-layered evaluation framework for detecting interpretive drift in long-context language model interactions. The method combines hierarchical source enforcement, fault-tree analysis, and cross-layer semantic consistency checks to systematically expose reasoning failures that emerge across multi-step outputs. By treating complex, high-conflict corpora as adversarial test environments, the framework reveals drift patterns that remain undetected under standard prompting and evaluation approaches.