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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.
Failure can be a powerful tool for uncovering the true requirements of machine learning systems, leading to better alignment with stakeholder needs.
Current code-editing benchmarks are so out of touch with real-world developer workflows that they risk misleading progress on LLMs for code.
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.
