12 citations · 36 across the 27 of their papers we have counts for
28 papers
SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
Xiaonan Luo, Yue Huang, Kehan Guo +9
Assessing cybersecurity vulnerability awareness in coding agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static benchmarks offer f…
Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning
Xiaonan Luo, Yue Huang, Kehan Guo +4
Model collapse is a central challenge in learning from synthetic data: as later-generation large language models (LLMs) are trained on an increasing proportion of model-generated d…
ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments
Taicheng Guo, Haomin Zhuang, Kehan Guo +4
Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within…
Reward Transport: Property Control in Flow Matching via Noise-Space Alignment
Kehan Guo, Yili Shen, Yujun Zhou +4
The coupling in flow matching -- the rule pairing noise vectors with data points -- is typically treated as a computational choice. We show that this coupling can instead serve as…
Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents
Yujun Zhou, Kehan Guo, Haomin Zhuang +8
Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated…
NARRA-Gym for Evaluating Interactive Narrative Agents
Yue Huang, Yuchen Ma, Jiayi Ye +14
Interactive narrative tasks require LLMs to sustain a coherent, evolving story while adapting to a user over multiple turns. However, suitable benchmarks for this setting are limit…
PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models
Han Bao, Penghao Zhang, Yue Huang +9
Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason abou…
Reliable Control-Point Selection for Steering Reasoning in Large Language Models
Haomin Zhuang, Hojun Yoo, Xiaonan Luo +2
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models, but constructing effective vectors requires identifying genuine behav…
Dual Optimal: Make Your LLM Peer-like with Dignity
Xiangqi Wang, Yue Huang, Haomin Zhuang +2
Current aligned language models exhibit a dual failure mode we term the Evasive Servant: they sycophantically validate flawed user beliefs while deflecting responsibility with boil…
ProbeLLM: Automating Principled Diagnosis of LLM Failures
Yue Huang, Zhengzhe Jiang, Yuchen Ma +8
Understanding how and why large language models (LLMs) fail is becoming a central challenge as models rapidly evolve and static evaluations fall behind. While automated probing has…
Alignment Risks from Capability-Seeking RL Training
Yujun Zhou, Yue Huang, Han Bao +8
While most AI alignment research focuses on preventing models from generating explicitly harmful content, a more subtle risk arises from capability-seeking RL training in vulnerabl…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Causally-Enhanced Reinforcement Policy Optimization
Xiangqi Wang, Yue Huang, Yujun Zhou +3
Large language models (LLMs) trained with reinforcement objectives often achieve superficially correct answers via shortcut strategies, pairing correct outputs with spurious or unf…
ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions
Yue Huang, Zhengzhe Jiang, Xiaonan Luo +12
Empowering large language models (LLMs) with chemical intelligence remains a challenge due to the scarcity of high-quality, domain-specific instruction-response datasets and the mi…
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
Yujun Zhou, Jiayi Ye, Zipeng Ling +8
Logical reasoning is a core capability for large language models (LLMs), yet existing benchmarks that rely solely on final-answer accuracy fail to capture the quality of the reason…
AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking in Large Language Models
Xiangqi Wang, Yue Huang, Yanbo Wang +4
LLMs often need effective configurations, like temperature and reasoning steps, to handle tasks requiring sophisticated reasoning and problem-solving, ranging from joke generation…
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis
Yicheng Lang, Kehan Guo, Yue Huang +5
Due to the widespread use of LLMs and the rising critical ethical and safety concerns, LLM unlearning methods have been developed to remove harmful knowledge and undesirable capabi…
Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond
Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel +8
The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic a…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
Prioritization First, Principles Second: An Adaptive Interpretation of Helpful, Honest, and Harmless Principles
Yue Huang, Chujie Gao, Yujun Zhou +5
The Helpful, Honest, and Harmless (HHH) principle is a foundational framework for aligning AI systems with human values. However, existing interpretations of the HHH principle ofte…
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
Haomin Zhuang, Yihua Zhang, Kehan Guo +4
Recent advancements in LLMs unlearning have shown remarkable success in removing unwanted data-model influences while preserving the model's utility for legitimate knowledge. Despi…
Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?
Yue Huang, Zhengqing Yuan, Yujun Zhou +8
Large Language Models (LLMs) are increasingly employed for simulations, enabling applications in role-playing agents and Computational Social Science (CSS). However, the reliabilit…
LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs
Yujun Zhou, Jingdong Yang, Yue Huang +12
Artificial Intelligence (AI) is revolutionizing scientific research, yet its growing integration into laboratory environments presents critical safety challenges. Large language mo…
ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering
Xiuying Chen, Tairan Wang, Taicheng Guo +7
Question Answering (QA) effectively evaluates language models' reasoning and knowledge depth. While QA datasets are plentiful in areas like general domain and biomedicine, academic…