most citedMut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation

Jianpeng Zhao, Chenyu Yuan, Weiming Luo +6

Questionnaire-based surveys are foundational to social science research and public policymaking, yet traditional survey methods remain costly, time-consuming, and often limited in…

cs.LG2026

Out-of-Distribution Graph Models Merging

Yidi Wang, Ziyue Qiao, Jiawei Gu +4

This paper studies a novel problem of out-of-distribution graph models merging, which aims to construct a generalized model from multiple graph models pre-trained on different doma…

cs.SE20261 cited

Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports

Bo Wang, Pengyang Wang, Chong Chen +9

Mutation-based fuzzing is effective for uncovering compiler bugs, but designing high-quality mutators for modern languages with complex constructs (e.g., templates, macros) remains…

cs.AI2025

Zero-Shot Human Mobility Forecasting via Large Language Model with Hierarchical Reasoning

Wenyao Li, Ran Zhang, Pengyang Wang +2

Human mobility forecasting is important for applications such as transportation planning, urban management, and personalized recommendations. However, existing methods often fail t…

cs.CL2025

SC: Speculative Sampling with Syntactic and Semantic Coherence for Efficient Inference of Large Language Models

Tao He, Guang Huang, Yu Yang +5

Large language models (LLMs) exhibit remarkable reasoning capabilities across diverse downstream tasks. However, their autoregressive nature leads to substantial inference latency,…

cs.AI2025

Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning

Hao Dong, Ziyue Qiao, Zhiyuan Ning +4

Temporal Knowledge Graphs (TKGs), as an extension of static Knowledge Graphs (KGs), incorporate the temporal feature to express the transience of knowledge by describing when facts…