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cs.CL2026

From Error Prediction to Causal Influence: Auditing SAE Features in Multiple-Choice QA

Aria Nourbakhsh, Adelaide Danilov, Oleksandr Marchenko Breneur +1

Sparse autoencoders (SAEs) can predict internal features associated with model errors, but association alone does not establish causal influence. We study this distinction in multi…

cs.CL2026

Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility

Salima Lamsiyah, Adelaide Danilov, Oleksandr Marchenko Breneur +1

Sparse autoencoders (SAEs) have emerged as a promising approach to mechanistic interpretability by decomposing language-model activations into sparse, potentially interpretable fea…

cs.CL2026

"Many Are My Names": The Anatomy of the Assistant and Its Personas via Sparse Autoencoders

Adelaide Danilov, Aria Nourbakhsh, Oleksandr Marchenko Breneur +1

How a language model internally represents who is speaking, the Assistant, an assigned roleplay persona, or a narrated story character, remains underexplored. We study speaker repr…

cs.CL2026

AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection

Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer +1

Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or l…

cs.CL2026

Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation

Aria Nourbakhsh, Salima Lamsiyah, Adelaide Danilov +1

The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been propos…

cs.CL2026

NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution

Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh +1

We present NotAI.AI, an explainable AI-generated text detection system. Instead of returning only a binary label or confidence score, the system shows which signals influenced the…