1 citations · 1 across the 10 of their papers we have counts for
13 papers
Revealing what benchmark scores hide: Jointly measuring the breadth and reliability of LLM capabilities using accessibility profiles
Kazem Faghih, Yize Cheng, Shoumik Saha +3
Language model benchmarks assign each question a single outcome from one fixed wording, even though the same underlying request can be expressed in many equivalent ways. We argue t…
Hierarchical Reinforcement Learning with Stable Temporal Abstraction for Language Model Agents
Shayan Mohajer Hamidi, Yize Cheng, Yuanda Xu +2
Hierarchical reinforcement learning improves long-horizon control by organizing primitive actions around persistent subgoals and assigning credit at multiple temporal scales. Recen…
Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions
Chenrui Fan, Yize Cheng, Ming Li +3
Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time. Yet when multiple…
Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
Kazem Faghih, Yize Cheng, Shoumik Saha +3
Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in differe…
When is Your LLM Steerable?
Chenrui Fan, Yize Cheng, Ming Li +2
Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, m…
Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use
Yize Cheng, Chenrui Fan, Mahdi JafariRaviz +2
Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior work studying adaptive tool use…
Failing to Explore: Language Models on Interactive Tasks
Mahdi JafariRaviz, Keivan Rezaei, Arshia Soltani Moakhar +3
We evaluate language models on their ability to explore interactive environments under a limited interaction budget. We introduce three parametric tasks with controllable explorati…
Schoenfeld's Anatomy of Mathematical Reasoning by Language Models
Ming Li, Chenrui Fan, Yize Cheng +2
Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statist…
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…
Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text
Yize Cheng, Vinu Sankar Sadasivan, Mehrdad Saberi +2
The increasing capabilities of Large Language Models (LLMs) have raised concerns about their misuse in AI-generated plagiarism and social engineering. While various AI-generated te…
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…
Attacking by Aligning: Clean-Label Backdoor Attacks on Object Detection
Yize Cheng, Wenbin Hu, Minhao Cheng
Deep neural networks (DNNs) have shown unprecedented success in object detection tasks. However, it was also discovered that DNNs are vulnerable to multiple kinds of attacks, inclu…