2 papers
cs.SE2026
SAFuzz: Semantic-Guided Adaptive Fuzzing for LLM-Generated Code
Ziyi Yang, Kalit Inani, Keshav Kabra +2
While AI-coding assistants accelerate software development, current testing frameworks struggle to keep pace with the resulting volume of AI-generated code. Traditional fuzzing tec…
cs.CL2025
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…