8 citations · 21 across the 27 of their papers we have counts for
28 papers
Why Search When You Can Transfer? Amortized Agentic Workflow Design from Structural Priors
Shiyi Du, Jiayuan Liu, Weihua Du +6
Automated agentic workflow design currently relies on per-task iterative search, which is computationally prohibitive and fails to reuse structural knowledge across tasks. We obser…
Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data
Zhenwen Liang, Yujun Zhou, Sidi Lu +3
Reinforcement Learning (RL) enhances LLM reasoning, yet a paradox emerges as models scale: strong base models saturate standard benchmarks (e.g., MATH), yielding correct but homoge…
AgentClick: A Skill-Based Human-in-the-Loop Review Layer for Terminal AI Agents
Haomin Zhuang, Hanwen Xing, Xiangliang Zhang
Recent autonomous AI agents such as Codex, and Claude Code have made it increasingly practical for users to delegate complex tasks, including writing emails, executing code, issuin…
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…
Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
Yue Huang, Haomin Zhuang, Jiayi Ye +6
Hard-gated safety checkers often over-refuse and misalign with a vendor's model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less us…
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…
SenseMath: Do LLMs Have Number Sense? Evaluating Shortcut Use, Judgment, and Generation
Haomin Zhuang, Xiangqi Wang, Yili Shen +2
Large language models often default to step-by-step computation even when efficient numerical shortcuts are available. This raises a basic question: do they exhibit number sense in…
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…
Building a Foundational Guardrail for General Agentic Systems via Synthetic Data
Yue Huang, Hang Hua, Yujun Zhou +11
While LLM agents can plan multi-step tasks, intervening at the planning stage-before any action is executed-is often the safest way to prevent harm, since certain risks can lead to…
Exploring Multi-Temperature Strategies for Token- and Rollout-Level Control in RLVR
Haomin Zhuang, Yujun Zhou, Taicheng Guo +4
Reinforcement Learning has demonstrated substantial improvements in the reasoning abilities of Large Language Models (LLMs), exhibiting significant applicability across various dom…
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…
My Favorite Streamer is an LLM: Discovering, Bonding, and Co-Creating in AI VTuber Fandom
Jiayi Ye, Chaoran Chen, Yue Huang +3
AI VTubers, where the performer is not human but algorithmically generated, introduce a new context for fandom. While human VTubers have been substantially studied for their cultur…
Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
Dawei Li, Yue Huang, Ming Li +3
Generative models such as Large Language Models, Diffusion Models, and generative adversarial networks have recently revolutionized the creation of synthetic data, offering scalabl…
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…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Li Li, Peilin Cai, Ryan A. Rossi +21
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike exis…
Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models
Zixiang Xu, Yanbo Wang, Yue Huang +4
Large Language Models (LLMs) have achieved remarkable success in Natural Language Processing (NLP), yet their cross-lingual performance consistency remains a significant challenge.…
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking
Tianle Gu, Zongqi Wang, Kexin Huang +4
Logit-based LLM watermarking traces and verifies AI-generated content by maintaining green and red token lists and increasing the likelihood of green tokens during generation. Howe…
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…
Adaptive Distraction: Probing LLM Contextual Robustness with Automated Tree Search
Yanbo Wang, Zixiang Xu, Yue Huang +6
Large Language Models (LLMs) often struggle to maintain their original performance when faced with semantically coherent but task-irrelevant contextual information. Although prior…
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Dawei Li, Renliang Sun, Yue Huang +6
Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…