[2510.05145] Efficient Tree-Structured Deep Research with Adaptive Resource Allocation

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2510.05145 (cs) [Submitted on 2 Oct 2025 (v1), last revised 29 Mar 2026 (this version, v2)] Title:Efficient Tree-Structured Deep Research with Adaptive Resource Allocation Authors:Lunyiu Nie, Nedim Lipka, Ryan A. Rossi, Swarat Chaudhuri View a PDF of the paper titled Efficient Tree-Structured Deep Research with Adaptive Resource Allocation, by Lunyiu Nie and 3 other authors View PDF HTML (experimental) Abstract:Deep research agents, which synthesize information across diverse sources, are significantly constrained by the sequential nature of reasoning. This bottleneck results in high latency, poor runtime adaptability, and inefficient resource allocation, making today's deep research systems impractical for interactive applications. To overcome this, we introduce ParallelResearch, 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 based on query complexity; (2) a runtime orchestration layer that prunes redundant paths to reallocate resources and enables speculative execution; and (3) a fully-asynchronous execution infrastructure that enables concurrency across both research breadth and depth. Experiments on two benchmarks show up to 5x speedups with c...