Large language models (LLMs) are increasingly integrated into software development workflows, yet a growing body of evidence shows that the code they generate often contains security vulnerabilities. Prior work has primarily focused on measuring the prevalence of such issues, providing useful evidence of risk but limit...
Ali Aljaberi, Jian Kang, You-Cheng Sun· Proceedings of the 41st IEEE...· 0 citations
Tool calling, invoking external tools on demand, is central to agentic LLMs, yet the mechanism that decides whether a model calls a tool or responds directly remains poorly understood. Agentic prompts are long and heavily scaffolded, combining role instructions, tool schemas, format templates, and the user's request ac...
X. Gong, Ting-Xu Han, Jia-Hao Zhang et al.· 0 citations
The Reasoning Backroom is established as a general AI provenance problem whose audit requires intervention because of a pervasive provenance failure in multi-agent systems.
Jinwei Hu, Yi Qi, Xin-Miao Huang et al.· arXiv.org· 0 citations
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