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20242026
most citedBias in Large Language Models: Origin, Evaluation, and Mitigation

26 citations · 55 across the 25 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

math.RA2026

Diagonalization of skew-Hermitian dual quaternion matrices and -Hermitian dual quaternion matrices

Cailing Yao, Bingzhe Hou

In this article, we present the diagonalization of skew-Hermitian dual quaternion matrices in the sense of unitary equivalence. For a unit quaternion , we also obtain the diagon…

cs.SE2026

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair

Wenqiang Luo, Jacky Keung, Xiaoyu Shi +4

Large language models (LLMs) have advanced automatic program repair (APR) to the point where agentic systems routinely resolve real-world, repository-level issues. Yet the generate…

cs.SE2026

How Reasoning Shapes Social Bias in LLM-Generated Code?

Weifeng Sun, Jieke Shi, Zhou Yang +4

Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demogra…

cs.SE2026

Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks

Chenyu Wang, Yunbo Lyu, Junda He +4

Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer a…

cs.SE2026

How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study

Yunbo Lyu, David Williams, Jieke Shi +5

The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been m…

cs.SE2026

Bridging Behavior and Implementation: Automated Java Glue Code Generation for Behavior-Driven Development

Xinyu Shi, Zhou Yang, An Ran Chen

Behavior-Driven Development (BDD) helps technical and non-technical stakeholders share a common understanding of software requirements through natural-language scenarios. Glue code…

cs.SE2026

Hidden Licensing Risks in the LLMware Ecosystem

Bo Wang, Yueyang Chen, Jieke Shi +5

Large Language Models (LLMs) are increasingly integrated into software systems, giving rise to a new class of systems referred to as LLMware. Beyond traditional source-code compone…

cs.CR2026

Watermarking LLM Agent Trajectories

Wenlong Meng, Chen Gong, Terry Yue Zhuo +6

LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model gene…