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Related Work#The limitations of single-shot retrieval have driven substantial exploration into agentic search systems, in which reasoning is interleaved with retrieval to resolve queries that require satisfying multiple constraints jointly or following a chain of dependent clues across documents. These systems vary in their termination strategy: some run for a fixed number of turns, while others terminate dynamically based on a learned sufficiency signal. By shifting control of the retrieval strategy to the model itself, these systems can reformulate queries based on intermediate results, decide when to explore versus exploit, and terminate search based on a confidence assessment. These systems model search as a sequential reasoning task, in which the right next query depends on what has been found so far. Benchmarks such as InfoDeepSeek, evaluate agentic information seeking in dynamic web environments, provide controlled testbeds for measuring multi-turn retrieval quality. However, most existing agentic search systems rely on frontier-scale models to drive the retrieval loop, making them expensive and latency-intensive to deploy at scale.