Dedicated exploration models (4B–30B parameters) can handle code search in repositories more efficiently than general solver models while significantly reducing context pollution.
AI-native development requires redesign of workflows and context access for agents, not just faster tool adoption—but then achieves 4.5x to 10x productivity gains.
Only one in five social scientists uses autonomous coding agents, despite their potential to revolutionize research processes, with clear disparities emerging by gender and institution—pointing to growing digital inequalities in academia.
