What question did this study set out to answer?

The aim is to develop a framework that identifies interpretive drift in interactions with long-context language models.

April 18, 2026Open Access

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

Key Points

The aim is to develop a framework that identifies interpretive drift in interactions with long-context language models.
Introduce a constraint-layered evaluation framework
Utilize hierarchical source enforcement and fault-tree analysis
Implement cross-layer semantic consistency checks
Analyze high-conflict corpora in adversarial test settings
Reveal previously undetected drift patterns
Demonstrate effectiveness over standard evaluation approaches
Expose reasoning failures across multi-step outputs

Abstract

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.

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

Key Points

Abstract

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