52 citations · 63 across the 12 of their papers we have counts for
15 papers
Speculative Forking: Turning Idle Wait into Parallel Exploration for Repository Environment Setup
Yuling Shi, Mingchu Zhong, Zhensu Sun +8
Before developers can use a software project, they must first get it to run. This often requires installing the right language runtime, system packages, build tools, and test comma…
SWE-at-Scale: Scalable Issue Synthesis from Historical Bug Fixes
Silin Chen, Zihan Zhang, Xiaodong Gu +7
As coding agents become increasingly capable, robust evaluation requires a broader and more diverse supply of verifiable software engineering (SWE) tasks. However, real-world tasks…
SRD-GUARD: A Defense Framework of LLMs via Semantic Rewriting and Joint Multi-Model Scoring for Latent Intent Exposure
Qi Wang, Chengcheng Wan, Jiangtao Wang
Large language models (LLMs) are increasingly deployed in safety-critical applications, yet jailbreak attacks can conceal harmful intent through role-playing, fictional scenarios,…
EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction
Yuling Shi, Zhensu Sun, Junsen Dong +3
Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a single pass of a frontier model over an agentic benchmark can cost hun…
Schrödinger's Code Repository: Have LLMs Learned SWE-bench or Memorized It?
Silin Chen, Yufei Yang, Xiaodong Gu +3
Repository-level coding benchmarks have become the standard for evaluating coding agents, yet they inherently suffer from data leakage because they are built upon popular open-sour…
Automated jailbreak attack targeting multiple defense strategies
Qi Wang, Chengcheng Wan, Weijia He +4
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their safety remains a critical concern due to their susceptibility to…
MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text
Chenjun Li, Cheng Wan, Johannes C. Paetzold +1
Large language models are widely used in everyday writing, making reliable AI-generated text detection crucial for academic integrity, content moderation, and provenance tracking.…
EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
Zimu Wang, Yuling Shi, Mengfan Li +4
Code efficiency is a fundamental aspect of software quality, yet how to harness large language models (LLMs) to optimize programs remains challenging. Prior approaches have sought…
Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding
Yuling Shi, Chaoxiang Xie, Zhensu Sun +7
Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bo…
Pruning the Unsurprising: Efficient LLM Reasoning via First-Token Surprisal
Wenhao Zeng, Yaoning Wang, Chao Hu +4
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities by scaling up the length of Chain-of-Thought (CoT). However, excessively long reasoning traces pose substant…
Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems
Shengming Zhao, Yuchen Shao, Yuheng Huang +4
Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges w…
CodeCipher: Learning to Obfuscate Source Code Against LLMs
Yalan Lin, Chengcheng Wan, Yixiong Fang +1
While large code language models have made significant strides in AI-assisted coding tasks, there are growing concerns about privacy challenges. The user code is transparent to the…
From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging
Yuling Shi, Songsong Wang, Chengcheng Wan +2
While large language models have made significant strides in code generation, the pass rate of the generated code is bottlenecked on subtle errors, often requiring human interventi…
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers
Yuling Shi, Hongyu Zhang, Chengcheng Wan +1
Large language models have catalyzed an unprecedented wave in code generation. While achieving significant advances, they blur the distinctions between machine- and human-authored…
On the Effectiveness of Large Language Models in Domain-Specific Code Generation
Xiaodong Gu, Meng Chen, Yalan Lin +6
Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire…