papers

Publications (7)

cs.AI2026

SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks

Yucheng Zeng, Shupeng Li, Daxiang Dong +11

Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. This scarcity stems fro…

cs.LG2022

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

Weiming Ren, Ruijing Zeng, Tongzi Wu +2

There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians…

cs.LG2024

QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning

Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu +5

Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named…

cs.CV2022

EAANet: Efficient Attention Augmented Convolutional Networks

Runqing Zhang, Tianshu Zhu

Humans can effectively find salient regions in complex scenes. Self-attention mechanisms were introduced into Computer Vision (CV) to achieve this. Attention Augmented Convolutiona…

cs.AI2026

AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning

Haotian Zhao, Songlin Zhou, Yuxin Zhang +9

Reinforcement learning (RL) has substantially improved the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. However, effective…

cs.AI2026

WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games

Wenyu Zhang, Guoliang You, Tianlun +8

Coding agents are increasingly used as application builders, yet many evaluations still focus on source code, repository-level tests, or intermediate traces rather than the deliver…

cs.LG2026

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime

Tianshu Zhu, Wenyu Zhang, Xiaoying Zuo +8

Agentic reinforcement learning (RL) for software engineering spends much of its compute on stateful trajectories whose grouped binary rewards are highly skewed and weakly contrasti…