3 papers
cs.LG2026
Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
John Cooper, Ilias Diakonikolas, Mingchen Ma +1
Hybrid sequence models--combining Transformer and state-space model layers--seek to gain the expressive versatility of attention as well as the computational efficiency of state-sp…
cs.CV2026
Seeing Beyond Redundancy: Task Complexity's Role in Vision Token Specialization in VLLMs
Darryl Hannan, John Cooper, Dylan White +1
Vision capabilities in vision large language models (VLLMs) have consistently lagged behind their linguistic capabilities. In particular, numerous benchmark studies have demonstrat…
cs.CV2025
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization
Darryl Hannan, John Cooper, Dylan White +3
Multimodal large language models (MLLMs) have altered the landscape of computer vision, obtaining impressive results across a wide range of tasks, especially in zero-shot settings.…