FlashResearch: Real-time Agent Orchestration for Efficient Deep Research

Deep research agents, which synthesize information across diverse sources, are significantly constrained by their sequential reasoning processes. To overcome this, we introduce FlashResearch, a novel framework for efficient deep research that transforms sequential processing into parallel, runtime orchestration by dynamically decomposing complex queries into tree-structured sub-tasks. Our core contributions are threefold: (1) an adaptive planner that dynamically allocates computational resources by determining research breadth and depth based on query complexity; (2) a real-time orchestration layer that monitors research progress and prunes redundant paths to reallocate resources and optimize efficiency; and (3) a multi-dimensional parallelization framework that enables concurrency across both research breadth and depth. Experiments show that FlashResearch consistently improves final report quality within fixed time budgets, and can deliver up to a 5 speedup while maintaining comparable quality. Deep research tasks, which involve synthesizing information from diverse sources and navigating complex, interdependent concepts, pose significant challenges for existing AI systems. These tasks often demand knowledge retrieval, advanced reasoning, sophisticated tool use, and dynamic planning over multiple steps under structural uncertainty and evolving objectives (Du et al., 2025). Applications include literature review (Haman & ˇ Skoln ık, 2025), open-domain question answering, and policy analysis (Gambrell, 2025), where the ability to evaluate conflicting perspectives, explore hypotheses, and revise beliefs as new evidence emerges is essential.