QueryWeaver: LLM-Based System for Multi-Tool Query Execution Planning
Researchers have developed QueryWeaver, a system that converts natural language queries into structured graphs to enable reliable execution of complex queries across multiple applications and tools. The approach uses depth-first search and deterministic planning to resolve dependencies and combine results, addressing a key limitation of current LLMs in handling multi-step, cross-tool queries. This advancement could enable more sophisticated data retrieval beyond traditional keyword-based search while working with smaller or locally hosted language models.
QueryWeaver is a new system designed to handle the complexity of queries that span multiple applications and require structured planning across tools. The system works by converting natural language queries into structured graphs, which are then executed through a deterministic planner using depth-first search algorithms to resolve dependencies and combine results from different sources. A key innovation is that the approach demonstrates high accuracy even when using smaller or locally hosted LLMs, rather than requiring large proprietary models. This addresses a significant challenge in AI-assisted data retrieval: while LLMs show strong reasoning and tool-use capabilities, reliably executing multi-step queries across different platforms has remained difficult. The research suggests this structured graph-based approach could enable queries more sophisticated than traditional keyword-based search methods.
What's missing
The paper does not provide information about comparative benchmarks against existing multi-tool query systems, the specific applications tested, or quantitative performance metrics (accuracy percentages, latency measurements, or failure rates).
What different sources said
- arXiv cs.LGCenter
QueryWeaver: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation
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