Shengcheng Yu

Refinement complexity in automotive requirements is driven more by architectural scope than by linguistic verbosity, revealing critical insights for improving product development efficiency.

Prompt injection remains the leading attack vector against LLM agents, but emerging threats like persistent state corruption demand urgent attention.

Agent-generated feedback not only enhances report quality but also improves task performance and knowledge transfer in crowdsourced testing environments.