3 citations · 5 across the 12 of their papers we have counts for
14 papers
SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents
Naoki Wake, Justin Wagle
Agent tasks require sequences of interdependent decisions. Autoregressive models support more flexible decision interfaces than conventional classifiers but incur the latency of to…
LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them
Yinheng Li, Justin Wagle
Jev-style decision models have gained surging traction by making decision-making dramatically faster and more efficient, returning categorical probability distributions over predef…
Covering Human Action Space for Computer Use: Data Synthesis and Benchmark
Miaosen Zhang, Xiaohan Zhao, Zhihong Tan +14
Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user…
ScreenSearch: Uncertainty-Aware OS Exploration
Michael Solodko, Justin Wagle
Desktop GUI agents operate under partial observability: visually similar screens can correspond to different underlying workflow states, so locally plausible actions can lead to sh…
MoLoRA: Composable Specialization via Per-Token Adapter Routing
Shrey Shah, Justin Wagle
Multi-adapter serving systems route entire sequences to a single adapter, forcing a choice when requests span multiple domains. This assumption fails in two important settings: (1)…
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…
Breaking and Fixing Defenses Against Control-Flow Hijacking in Multi-Agent Systems
Rishi Jha, Harold Triedman, Justin Wagle +1
Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system and exfiltrate sensitive informa…
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.…
Phi-Ground Tech Report: Advancing Perception in GUI Grounding
Miaosen Zhang, Ziqiang Xu, Jialiang Zhu +8
With the development of multimodal reasoning models, Computer Use Agents (CUAs), akin to Jarvis from \textit{"Iron Man"}, are becoming a reality. GUI grounding is a core component…
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
Rogerio Bonatti, Dan Zhao, Francesco Bonacci +9
Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning…
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
Yinheng Li, Rogerio Bonatti, Sara Abdali +2
Using Large Language Models (LLMs) to generate synthetic data for model training has become increasingly popular in recent years. While LLMs are capable of producing realistic trai…
One of these (Few) Things is Not Like the Others
Nat Roth, Justin Wagle
To perform well, most deep learning based image classification systems require large amounts of data and computing resources. These constraints make it difficult to quickly persona…
Sometimes You Want to Go Where Everybody Knows your Name
Reuben Brasher, Nat Roth, Justin Wagle
We introduce a new metric for measuring how well a model personalizes to a user's specific preferences. We define personalization as a weighting between performance on user specifi…