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Amil Dravid

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#artificial intelligence Preprint Oct 2026

Rethinking Neural Thickets Without Weight Perturbations

Logit PSV is suggested, a simple, training-free alternative to full-weight perturbation ensembles across language and vision, where logit PSV substantially improves performance under domain shift, including on Earth observation, while full-weight perturbations provide little additional benefit.

Nicolas Dufour, Elouan Gardes, Amil Dravid et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Base Models Can Reason By Taking a Cue From Training Data

In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior that follows. First, we demonstrate that fixing particular starting token cues makes a base model's performance competitive with that of its reinforcement learning (RL)-tr...

Sophie L. Wang, Amil Dravid, Rulin Shao et al. · 0 citations

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