arXiv:2606.17209v1 Announce Type: new 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 t
The increasing complexity and resource demands of agentic AI drives the need for more efficient and effective search strategies.
Improving the efficiency of agentic AI search can significantly reduce computational costs and enhance the performance of autonomous systems, making advanced AI applications more viable.
This intervention allows for more diverse exploration earlier in agentic search processes, moving beyond the diminishing returns of standard parallel sampling.
- · AI developers
- · Cloud providers
- · Enterprises adopting AI agents
- · Legacy AI search methodologies
More robust and less resource-intensive agentic AI systems become accessible for broader deployment.
Accelerated development and adoption of AI agents across various industries, replacing traditional white-collar tasks.
Increased demand for specialized compute resources and optimized AI architectures as agentic systems scale.
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Read at arXiv cs.AI