Author

M. Rozenshteĭn

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#artificial intelligence Preprint Oct 2026

Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs

Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed in different ways. Existing work typically evaluates generalization through aggregate accuracy on a single prompt format, task, or set of variations, which conflates robus...

Nagham Omar, Mahmoud Jabarin, M. Rozenshteĭn et al. · 0 citations

#machine learning Preprint Sep 2026

Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning

Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learning. We show that this choice has exploitable structure: retraining is most useful in early rounds, when each batch can substantially reshape the labeled distribution, w...

Nagham Omar, M. Rozenshteĭn, Evgeny Mishlyakov et al. · 0 citations