2 citations · 4 across the 17 of their papers we have counts for
19 papers
The Greatness of Science Cannot Be Planned: Agentic Auto-Research is Fuzz Testing
Yifeng He, Jicheng Wang, Yinzhe Zhao +3
Agentic auto-research is emerging, but most systems treat scientific discovery as goal-oriented optimization against a final benchmark. This paradigm rewards a sparse final verdict…
Invariant Pretraining for Robust Code Representations
Yifeng He, Yundi Xu, Christopher Castro Gaw Gonzalo +2
Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their small size and low inference c…
LLM-Based Invariant Testing for Software Functional Bugs
Ruogu Yang, Yifeng He, Yundi Xu +2
Manually writing unit tests to uncover functional bugs in software libraries is not only time-consuming but also requires a deep understanding of the intended semantics of the APIs…
Is Progressive Disclosure All You Need for Long-Context Agents?
Yifeng He, Yinzhe Zhao, Jicheng Wang +1
Long-document question answering usually forces a choice between loading the whole document into the context window and bolting on a separate retriever. Agentic AI suggests a broad…
Code Generation by Differential Test Time Scaling
Yifeng He, Ethan Wang, Jicheng Wang +2
Test-time scaling has emerged as a promising approach for improving code generation by exploring large solution spaces at inference time. However, existing methods often rely on pu…
ContractBench: Can LLM Agents Preserve Observation Contracts?
Jicheng Wang, Yifeng He, Zili Wang +3
Tool-augmented LLM agents call APIs whose intermediate outputs, such as presigned URLs, session tokens, and OAuth state parameters, are observation contracts: artifacts whose later…
Content Fuzzing for Escaping Information Cocoons on Digital Social Media
Yifeng He, Ziye Tang, Hao Chen
Information cocoons on social media limit users' exposure to posts with diverse viewpoints. Modern platforms use stance detection as an important signal in recommendation and ranki…
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Xiangyi Li, Yimin Liu, Wenbo Chen +75
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…
SpecTokenizer: A Lightweight Streaming Codec in the Compressed Spectrum Domain
Zixiang Wan, Guochang Zhang, Yifeng He +1
Neural Audio Codecs (NACs) have gained growing attention in recent years as technologies for audio compression and audio representation in speech language models. While mainstream…
Evaluating Program Semantics Reasoning with Type Inference in System F
Yifeng He, Luning Yang, Christopher Castro Gaw Gonzalo +1
Large Language Models (LLMs) are increasingly integrated into the software engineering ecosystem. Their test-time compute (TTC) reasoning capabilities show significant potential fo…
SteerDiff: Steering towards Safe Text-to-Image Diffusion Models
Hongxiang Zhang, Yifeng He, Hao Chen
Text-to-image (T2I) diffusion models have drawn attention for their ability to generate high-quality images with precise text alignment. However, these models can also be misused t…
RepoGenReflex: Enhancing Repository-Level Code Completion with Verbal Reinforcement and Retrieval-Augmented Generation
Jicheng Wang, Yifeng He, Hao Chen
In real-world software engineering tasks, solving a problem often requires understanding and modifying multiple functions, classes, and files across a large codebase. Therefore, on…
Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition
Yuchun Shu, Bo Hu, Yifeng He +3
Accurately finding the wrong words in the automatic speech recognition (ASR) hypothesis and recovering them well-founded is the goal of speech error correction. In this paper, we p…
Security of AI Agents
Yifeng He, Ethan Wang, Yuyang Rong +2
AI agents have been boosted by large language models. AI agents can function as intelligent assistants and complete tasks on behalf of their users with access to tools and the abil…
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
Yifeng He, Jicheng Wang, Yuyang Rong +1
Testing is essential to modern software engineering for building reliable software. Given the high costs of manually creating test cases, automated test case generation, particular…
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
Hongxiang Zhang, Yuyang Rong, Yifeng He +1
Greybox fuzzing has achieved success in revealing bugs and vulnerabilities in programs. However, randomized mutation strategies have limited the fuzzer's performance on structured…
UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing
Yifeng He, Jiabo Huang, Yuyang Rong +3
The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code L…
Code Representation Pre-training with Complements from Program Executions
Jiabo Huang, Jianyu Zhao, Yuyang Rong +3
Large language models (LLMs) for natural language processing have been grafted onto programming language modeling for advancing code intelligence. Although it can be represented in…
Understanding Programs by Exploiting (Fuzzing) Test Cases
Jianyu Zhao, Yuyang Rong, Yiwen Guo +2
Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, t…