dailyai.report

Standard parallel sampling in agentic search suffers from diminishing returns due to query redundancy. DivInit solves this by selecting a diverse subset of seeds from a larger candidate pool during the first turn. Testing across five open-weight models shows this training-free method improves retrieval breadth. Practitioners can now scale search trajectories without wasting compute on overlapping evidence.