Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search

[2606.17209] Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search

[Submitted on 15 Jun 2026]

Title:Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search

View a PDF of the paper titled Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search, by Sidhaarth Murali and 5 other authors

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Abstract:Test-time scaling for agentic search typically increases depth (i.e., more turns and tokens per trajectory) or breadth (i.e., more parallel rollouts). Here we focus on breadth scaling, showing that standard parallel sampling yields diminishing returns, tracing this to query redundancy at the first turn. When models issue similar first queries across rollouts, the threads retrieve overlapping evidence, and subsequent turns are conditioned on this shared retrieval. We address this limitation with DivInit, a training-free intervention at the first turn. Rather than sampling k independent first queries, DivInit draws n candidates from a single call, picks k

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