dailyai.report

Standard parallel sampling in agentic search often produces redundant first-turn queries. DivInit solves this by selecting a diverse subset of seeds from a single model call rather than independent samples. This training-free method improves breadth scaling across five open-weight models. It ensures agents retrieve distinct evidence, increasing the efficiency of test-time compute.