6 papers
Agentic Auto-Research is Fuzz Testing
Yifeng He, Jicheng Wang, Yinzhe Zhao +2
Autonomous research agents can generate experiments faster than researchers can validate them. Researchers have responded by scaling the proposer and ranking more samples with a le…
CurveShift: Is Agent Progress Scalar? Separating Level from Shape
Hanwen Xing, Pengyun Wang, BingXu Meng +8
Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summa…
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…
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…