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Queen鈥檚 University
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Over 62% of AI supply chains include artifacts with no declared license, raising serious concerns about compliance and accountability in AI development.
ML evaluation harnesses, the unsung heroes of model development, are plagued by surprisingly mundane software engineering issues like missing documentation and unimplemented features, hindering reliable assessment.
A clever routing strategy lets a tiny 3B code model outperform a massive 480B model on routine code completion tasks, slashing accelerator usage by 58%.
AI coding agents are surprisingly bad at logging, requiring humans to silently fix 72.5% of their logging mistakes.
Turns out, almost all AI agent tool descriptions are "smelly," and while fixing them improves performance, it also introduces a tricky efficiency trade-off that can be solved by carefully choosing which components to include.
Discover how AI coding agents are *actually* being used in real-world software projects with a new dataset of nearly one million agent-authored pull requests.
