1 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
From Patching to Pruning Visual Computation in Vision Language Models
Rahul Chowdhury, Timothy A Rupprecht, Xuan Shen +3
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-…
Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B
Rahul Chowdhury, Timothy A Rupprecht, Senhao Cao +5
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-co…
ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision Language Models
Rahul Chowdhury, Timothy A Rupprecht, Xuan Shen +2
Spatial reasoning remains a persistent challenge for many vision language models (VLMs), and improving it typically requires fine-tuning with substantial additional parameters. Our…
PhyWorld: Physics-Faithful World Model for Video Generation
Pu Zhao, Juyi Lin, Timothy Rupprecht +10
World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation models are emerging as a promising…
Human Cognition in Machines: A Unified Perspective of World Models
Timothy Rupprecht, Pu Zhao, Amir Taherin +20
This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To…