8 papers
CUA-Skill: Develop Skills for Computer Using Agent
Tianyi Chen, Yinheng Li, Michael Solodko +12
Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and lag behind h…
AppSelectBench: Application-Level Tool Selection Benchmark
Tianyi Chen, Michael Solodko, Sen Wang +14
Computer Using Agents (CUAs) are increasingly equipped with external tools, enabling them to perform complex and realistic tasks. For CUAs to operate effectively, application selec…
Hierarchical Self-Attention: Generalizing Neural Attention Mechanics to Multi-Scale Problems
Saeed Amizadeh, Sara Abdali, Yinheng Li +1
Transformers and their attention mechanism have been revolutionary in the field of Machine Learning. While originally proposed for the language data, they quickly found their way t…
Instruction Agent: Enhancing Agent with Expert Demonstration
Yinheng Li, Hailey Hultquist, Justin Wagle +1
Graphical user interface (GUI) agents have advanced rapidly but still struggle with complex tasks involving novel UI elements, long-horizon actions, and personalized trajectories.…
Self-reflecting Large Language Models: A Hegelian Dialectical Approach
Sara Abdali, Can Goksen, Michael Solodko +4
In this paper, we introduce a self-reflection framework for Large Language Models (LLMs) grounded in the Hegelian Dialectic, a philosophical method in which an initial proposition…
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression
Xiaoyi Qu, David Aponte, Colby Banbury +5
Structured pruning and quantization are fundamental techniques used to reduce the size of deep neural networks (DNNs) and typically are applied independently. Applying these techni…