Attribution aims to identify which input tokens are responsible for a language model’s predictions, yet its evaluation in NLP remains dominated by discrete Top-k removal tests that poorly reflect global importance and interaction effects. In this work, we reformulate text attribution evaluation as a continuous functional over attribution-induced trajectories and introduce two complementary AUC-based metrics tailored to autoregressive models. Perturbation AUC measures token necessity via progressive embedding-level corruption with a stable logit-based signal, while Recovery AUC evaluates sufficiency by tracking the restoration of internal representations in hidden-state space. Together, these metrics subsume Top- tests and enable principled, ordering-sensitive assessment of attribution quality. Using these diagnostics, we show that most existing attribution methods collapse to near-random rankings under continuous evaluation and fail to capture even first-order interaction effects. Motivated by this failure mode, we propose a mid-layer attribution framework that exploits a layer-wise saturation property of transformer computation to decompose attribution into information allocation and causal effect. Across multiple models and tasks, our method consistently outperforms existing baselines under both Perturbation and Recovery AUC, yielding more faithful and interaction-aware explanations of language model behavior.

Model editing provides a promising mechanism for updating large language models (LLMs) without expensive retraining. Existing approaches, particularly locate-and-edit methods based on least-squares optimization, aim to introduce targeted knowledge changes while preserving pre-trained behavior. In this work, we show that this objective is fundamentally fragile under standard single-edit evaluation protocols. We first develop a unified theoretical framework that characterizes activation-based editing as a constrained intervention on intermediate representations. Within this framework, we demonstrate that least-squares edits cannot, in general, isolate target updates from unrelated activations, giving rise to unavoidable interference that accumulates with successive edits. Crucially, this degradation can remain undetected in single-edit settings when assessed using conventional success and locality metrics. To expose such hidden instabilities, we introduce an uncertainty-based evaluation protocol that combines structured semantic perturbations with uncertainty quantification based on Sampling with Perturbation for UQ. By measuring edit-induced growth in aleatoric and epistemic uncertainty, our method reveals local knowledge conflicts that are invisible to existing benchmarks. Extensive experiments across multiple models, datasets, and editing algorithms show that both least-squares and other parameter-update-based methods consistently increase post-edit uncertainty. Together, our results suggest that current evaluation practices substantially overestimate the reliability of single-edit model editing, and that uncertainty-based diagnostics are necessary for assessing edit stability.