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Shauli Ravfogel

Center for Data Science, New York University and Bar-Ilan University

Critic Current evidence and current methods do not support the claims being made

Shauli Ravfogel is a natural language processing and mechanistic interpretability researcher at New York University and Bar-Ilan University, known for developing iterative nullspace projection (INLP), linear concept erasure, and causal intervention methods for auditing what neural representations actually control.

In Can LLMs Introspect? A Reality Check (arXiv:2605.26242, May 2026), co-authored with Shashwat Singh and Tal Linzen, Ravfogel applied causal representation auditing to claims of emergent introspection in large language models. The authors showed that in both intervention-detection and hidden-state-prediction paradigms, models frequently rely on distributional anomalies at the input surface rather than privileged readout of their own internal activations, and that external classifiers reading only the input match the model’s self-predictions until relabeled controls reduce performance toward chance.

Ravfogel is classified as a Critic on this site. His causal-erasure and control-condition methods separate genuine internal state monitoring from surface statistical confounds in AI interpretability research.

Known for. Can LLMs Introspect? A Reality Check, linear concept erasure, causal probing of neural representations

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