Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demographic attributes. While prior work mainly studies direct code generation, bias in reasoning-based generation remains underexplored. We conduc...
Wei-Feng Sun, Jie-Ke Shi, Zhou Yang et al.· 0 citations
A typed domain-specific language that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators, is designed, which formulates lossless tensor compression as program synthesis.
Jie-Ke Shi, Jun-Da He, Wenjia Jiang et al.· 0 citations
Evaluations across agent systems, benchmarks, and backbone LLMs under 65 fault configurations show that all systems degrade under fault injection, suggesting that robustness depends on system implementation rather than model capability.
Gou Tan, Zhensu Sun, Jie-Ke Shi et al.· Proceedings of the 41st IEEE...· 6 citations
MultiFixer, a novel Coordinator-Proposer based multi-agent framework for multi-hunk repair, performs tool-augmented bug analysis, constructs fine-grained repair context, iteratively generates patches through a Coordinator-Proposer architecture, and applies two-stage patch refinement for syntactic and semantic correctne...
Hai-Chuan Hu, Chun-Rong Fang, Ye Shang et al.· arXiv.org· 0 citations
ReProAgent is a multi-stage agent framework for reproduction test generation from issue reports that decomposes the task into four agent stages: bug localization, root cause analysis, test planning, and test generation, and generalizes across multiple backbone LLMs.
Quanjun Zhang, Yi Zheng, Ye Shang et al.· 1 citation
The first systematic study of model editing as a model-level hardening mechanism for secure code generation is conducted, evaluating 3 state-of-the-art editing methods across diverse LLM families and comparing them with CoSec, a representative inference-time approach, focusing on security, robustness, generalization, a...
Wei-Feng Sun, Quan-Jun Zhang, Yuchen Chen et al.· 0 citations
Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question. Existing datasets and benchmarks are limited in scale, domain coverage, or executable verification, leaving the true gap between current LLMs and reliable scientific code generators...
Weifeng Sun, Ye Fan, Yuchen Chen et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
