4 citations · 6 across the 9 of their papers we have counts for
9 papers
Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable p…
Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception
Yize Cheng, Arshia Soltani Moakhar, Chenrui Fan +5
Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlooked limitation of these agents is…
Maestro: Joint Graph & Config Optimization for Reliable AI Agents
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Building reliable LLM agents requires decisions at two levels: the graph (which modules exist and how information flows) and the configuration of each node (models, prompts, tools,…
DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors
Yize Cheng, Wenxiao Wang, Mazda Moayeri +1
Open benchmarks are essential for evaluating and advancing large language models, offering reproducibility and transparency. However, their accessibility makes them likely targets…
Tool Preferences in Agentic LLMs are Unreliable
Kazem Faghih, Wenxiao Wang, Yize Cheng +5
Large language models (LLMs) can now access a wide range of external tools, thanks to the Model Context Protocol (MCP). This greatly expands their abilities as various agents. Howe…
Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption
Wenxiao Wang, Parsa Hosseini, Soheil Feizi
Chain-of-thought prompting has demonstrated great success in facilitating the reasoning abilities of large language models. In this work, we explore how these enhanced reasoning ab…
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
Wenxiao Wang, Weiming Zhuang, Lingjuan Lyu
The advancement of deep learning technologies is bringing new models every day, motivating the study of scalable model selection. An ideal model selection scheme should minimally s…
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
Mehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei +4
In light of recent advancements in generative AI models, it has become essential to distinguish genuine content from AI-generated one to prevent the malicious usage of fake materia…
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Mazda Moayeri, Wenxiao Wang, Sahil Singla +1
We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model traini…