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Priyanshu Srivastava

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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

Jul 2026

The Tokenizer Tax: Quantifying and Explaining the Cross-Lingual Cost of Subword Tokenization for Indian Languages

This work quantifies the tokenizer tax for Indian languages using the FLORES-200 parallel corpus, measuring tokenization fertility across six widely used tokenizers and fourteen languages, and identifies the primary mechanism behind this disparity: failed byte-pair merges that leave text fragmented into single-byte tok...

Priyanshu Srivastava · arXiv.org · 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