most citedRedCoder: Automated Multi-Turn Red Teaming for Code LLMs

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SE20261 cited

RedCoder: Automated Multi-Turn Red Teaming for Code LLMs

Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +5

Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software development and testing. However, recent studie…

cs.AR2026

Evidence-Driven LLM Agent for C-to-Synthesizable-C Conversion and Verification

Zhe Zhao, Hongbing Lang, Zhihan Xiao +3

Software-compilable C programs routinely fail to complete the four-stage pipeline of a high-level synthesis (HLS) toolchain -- compilation, C simulation (CSim), synthesis, and C/RT…

cs.AR2026

Shift-Left High-Level Synthesis Verification via Knowledge-Augmented LLM Agent

Zhihan Xiao, Hongbing Lang, Zhe Zhao +2

High-Level Synthesis (HLS) relies on transforming original C specifications into synthesizable HLS-oriented C (HLS-C) implementations. Functional consistency verification between o…

cs.CL2026

DebugLM: Learning Traceable Training Data Provenance for LLMs

Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +3

Large language models (LLMs) are trained through multi-stage pipelines over heterogeneous data sources, yet developers lack a principled way to pinpoint the specific data responsib…

cs.LG2026

Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models

Rui Cai, Bangzheng Li, Xiaofei Wen +2

Multimodal Large Language Models demonstrate strong performance on multimodal benchmarks, yet often exhibit poor robustness when exposed to spurious modality interference, such as…