Title: FlowSearcher: Synthesizing Memory-Guided Agentic Workflows for Web Information Seeking
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Large Language Model Reasoning, Structured Planning, Task Adaptability

Abstract: Web search is a cornerstone for deep research agents, enabling them to acquire and reason over knowledge beyond static corpora. Yet most existing systems follow rigid ReAct-style tool chains locked into fixed workflow structures, which hinders their ability to flexibly handle diverse query types and tool-use strategies. We introduce $\textbf{FlowSearcher}$, a novel web search framework built on agentic workflow synthesis. FlowSearcher decomposes queries into sub-goals, each orchestrated by a tailored workflow graph that adapts the depth and order of tool use, giving the system structural flexibility to handle diverse sub-goals ranging from simple lookups and focused navigation to multi-hop information synthesis. Complementing this, a hierarchical memory distills past workflows into structured experience, providing reusable context that improves orchestration and guides tool use on new queries. This shift from reactive tool calls to memory-driven workflow design and execution marks a principled step toward deliberative web research.  Empirical results on GAIA, BrowseComp, and GPQA show consistent improvements over state-of-the-art baselines, validating the benefits of query-specific workflow synthesis and hierarchical memory integration.

TL;DR: FlowSearcher is the first to reframe web search as query-specific agentic workflow synthesis with structured planning and hierarchical memory.