Author

Rahul Chowdhury

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#machine learning Preprint Oct 2026

From Patching to Pruning Visual Computation in Vision Language Models

Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every decoder layer, even when token-specific visual computation is unnecessary at many depths. We introduce Patch-to-Prune (P2P), inspired by Mechanistic Interpretability, a tr...

Rahul Chowdhury, Timothy Rupprecht, Xuan Shen et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B

Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-context learning capabilities, the mechanisms with which they accomplish time-series prediction remain unclear. Specifically, whether they tru...

Rahul Chowdhury, Timothy Rupprecht, Senhao Cao et al. · 0 citations