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Sharp Bounds on the Winner's Bias under Independent and Nonnegatively Correlated Noise
Ivan Viakhirev, Kirill Borodin, Grach Mkrtchian
Reporting the best of noisy, unbiased scores overstates the winner's value by up to , where is the total noise variance and the sharp constant depends on how…
Agreement rather than normalisation keeps depth reusable in looped language models
Ivan Viakhirev, Kirill Borodin, Maxim Abramov +1
Looped language models apply one block of layers repeatedly, and the design pays off only if depth is reusable: loop steps beyond the trained budget must remain usable at inference…
Think Shallow, Solve Deep: Controlling Recurrent Dynamics for Reliable Test-Time Depth
Ivan Viakhirev, Kirill Borodin, Amirah Almutairi +3
Recurrent-depth reasoners aim to solve harder problems by iterating their update longer at test time, but additional iterations can improve, preserve, or degrade an answer. We show…
From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales
Ivan Viakhirev, Kirill Borodin, Grach Mkrtchian
Hallucinations in large ASR models present a critical safety risk. In this work, we propose the \textit{Spectral Sensitivity Theorem}, which predicts a phase transition in deep net…