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Romit Chatterjee

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#artificial intelligence Preprint Jul 2026

Suppressed, Not Erased: A Representational Trace of Edited Facts Survives Even Weight-Free Knowledge Editing

Knowledge-editing benchmarks certify local correctness, whether an edited model produces the new fact on near-edit prompts but not how much of the original fact remains decodable inside the model. We study residual knowledge directly with a linear trace probe: after editing a fact, we ask whether the original object is...

Priyanshu Srivastava, Romit Chatterjee · 0 citations
Preprint Jul 2026

The Information Shadow: Measuring Structural Limits on What Language Models Can Learn

The information shadow is introduced: the region of phenomena that a text-trained learner cannot acquire regardless of scale, comprising (I) structures language cannot express, (II) functions that are statistically non-identifiable from the training distribution, and (III) functions that are representable but unreachab...

Priyanshu Srivastava, Romit Chatterjee · 0 citations

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