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cs.AI2026
Skip-It? Theoretical Conditions for Layer Skipping in Vision-Language Models
Max Hartman, Vidhata Jayaraman, Moulik Choraria +2
Vision-language models achieve incredible performance across a wide range of tasks, but their large size makes inference costly. Recent work has shown that multimodal processing co…
cs.AI2026
Energy-Aware Routing to Large Reasoning Models
Austin R. Ellis-Mohr, Max Hartman, Lav R. Varshney
Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it is important to choose the right…
cs.AI2025
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
Saurabh Jha, Rohan Arora, Yuji Watanabe +40
Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a…