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Shashwat Singh

Center for Data Science, New York University

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

Shashwat Singh is a machine learning and interpretability researcher working with Shauli Ravfogel and Tal Linzen on causal probing, representation erasure, and the empirical validity of self-knowledge claims in large language models.

As first author of Can LLMs Introspect? A Reality Check (arXiv:2605.26242, May 2026), Singh systematically tested whether language models that pass intervention-detection or hidden-state-prediction benchmarks are genuinely reading their own internal states. By constructing relabeled control tasks and training external baseline classifiers restricted to prompt features, Singh, Linzen, and Ravfogel proved that existing introspection benchmarks are heavily confounded by input-level anomalies and surface heuristics, establishing a rigorous evidentiary bar for mechanistic introspection studies.

Singh is classified as a Critic on this site for designing the control experiments that constrain claims of metacognitive self-access in frontier language models.

Known for. Can LLMs Introspect? A Reality Check, concept erasure and causal representation auditing in language models

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