dailyai.report

The DivInit method improves agentic search by selecting diverse initial seeds instead of using standard parallel sampling. This training-free intervention prevents multiple search threads from retrieving overlapping evidence. It solves the diminishing returns seen in breadth scaling across five open-weight models. Practitioners can now increase search efficiency without adding significant computational overhead.