dailyai.report

The DivInit method improves breadth scaling by selecting diverse seeds from a single model call rather than using independent parallel sampling. This prevents agents from retrieving overlapping evidence during the first turn. Testing across five open-weight models shows that reducing initial query redundancy increases the efficiency of test-time scaling for practitioners.