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arXiv CS.AI
6/17/2026
Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search

Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search

Short summary

Research introduces DivInit, a training-free method improving agentic search by sampling diverse first queries instead of independent ones. Addresses query redundancy that causes diminishing returns in parallel rollouts. Achieves 5-7 point gains on multi-hop QA benchmarks with open-weight models.

  • •DivInit improves parallel agentic search by ensuring diverse first queries
  • •Solves query redundancy problem that causes diminishing returns in standard parallel sampling
  • •5-7 point gains across eight benchmarks with code available on GitHub

Generated with AI, which can make mistakes.

#research-breakthrough#ai-agents#open-source
Read full article at arXiv CS.AI

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