collaborators

9 papers

cs.SE2026

Ockhamareto: Pareto-Gated Segment-Level Credit Assignment for Concise Unit-Test Generation with Reinforcement Learning

Dong Huang, Mark Harman, Jie M. Zhang +3

We introduce \textbf{Ockhamareto}, a single-shot GRPO framework for unit-test generation and selection, based on the principles of \emph{Ockham's Razor} and \emph{Pareto Optimality…

cs.AI2026

Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction

Mingzhe Du, Luu Anh Tuan, Tianyi Wu +4

Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar that reaches a vulnerable path,…

cs.LG2026

ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning

Wenyang Hu, Junxiang Jia, Zhen Shu +3

Reinforcement Learning with Verifiable Rewards (RLVR) for language-model reasoning can fail at both extremes of task difficulty: easy prompts often produce all-correct, low-diversi…

cs.LG2026

Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts

Zhaomin Wu, Mingzhe Du, See-Kiong Ng +1

Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is i…

cs.DL2026

Paper Espresso: From Paper Overload to Research Insight

Mingzhe Du, Luu Anh Tuan, Dong Huang +1

The accelerating pace of scientific publishing makes it increasingly difficult for researchers to stay current. We present Paper Espresso, an open-source platform that automaticall…

cs.CR2026

Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model

Tianyi Wu, Mingzhe Du, Yue Liu +4

Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment. Existing…