1 citations · 1 across the 14 of their papers we have counts for
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LogicTrack: Auditing Reasoning Trajectories of Large Language Models with Formal Logic Solvers
Jingyu Hu, Shu Yang, Weiru Liu +1
Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely rely on outcome-based feedback…
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Xiangyi Li, Yimin Liu, Wenbo Chen +75
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…
Faithful-Patchscopes: Understanding and Mitigating Model Bias in Hidden Representations Explanation of Large Language Models
Xilin Gong, Shu Yang, Zehua Cao +2
Large Language Models (LLMs) have demonstrated strong capabilities for hidden representation interpretation through Patchscopes, a framework that uses LLMs themselves to generate h…
Hearing is Believing? Evaluating and Analyzing Audio Language Model Sycophancy with SYAUDIO
Junchi Yao, Lokranjan Lakshmikanthan, Annie Zhao +5
Audio Language Models (ALMs) have recently shown strong capabilities in unified reasoning over speech, sound, and natural language; yet we find that they can inherit sycophancy, th…
Not All Code Is Equal: A Data-Centric Study of Code Complexity and LLM Reasoning
Lukas Twist, Shu Yang, Hanqi Yan +4
Large Language Models (LLMs) increasingly exhibit strong reasoning abilities, often attributed to their capacity to generate chain-of-thought-style intermediate reasoning. Recent w…
Dependently-Typed AARA: A Non-Affine Approach for Resource Analysis of Higher-Order Programs
Han Xu, Di Wang
Static resource analysis determines the resource consumption (e.g., time complexity) of a program without executing it. Among the numerous existing approaches for resource analysis…
A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation
Di Wang, Zhenhua Wu, Yu Liu +2
Scientific and engineering verticals often suffer from data scarcity and strict executability requirements: models must generate not only fluent text, but also syntactically valid,…
AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor
Shu Yang, Jingyu Hu, Tong Li +3
We introduce AutoMonitor-Bench, the first benchmark designed to systematically evaluate the reliability of LLM-based misbehavior monitors across diverse tasks and failure modes. Au…