This work introduces a system that converts natural language queries into structured graphs and executes them via a deterministic planner, which uses depth-first search to resolve dependencies and combine results across tools, improving reliability and enabling queries beyond traditional keyword-based search.
Abstract
Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information. While LLMs show strong reasoning and tool use, reliably executing multi-step, cross-tool queries remains challenging. We introduce a system that converts natural language queries into structured graphs and executes them via a deterministic planner. Our approach uses depth-first search to resolve dependencies and combine results across tools, improving reliability and enabling queries beyond traditional keyword-based search. We demonstrate high accuracy even with smaller or locally hosted LLMs.
This work proposes XGAP, an ambiguity-aware cross-platform graph query planner that integrates language models to generate structural interpretations and compiles them directly into distributed, compatible, and executable optimized plans.
Haolai Che, †. QuanqingXu, ‡. YinghuiWu et al.· 0 citations
ClosureBench is introduced, a constructive benchmark for compositional graph-relational reasoning with programmatically verified ground truth with programmatically verified ground truth: each task's reference answer is computed by executing a program in the Ein tensor-logic language, ensuring machine-verified correctne...
Conjunctive regular path queries (CRPQs) form a critical backbone of modern graph query languages, integrating subgraph matching with regular path queries (RPQs). Despite their ubiquity in applications like social networks, finance, and scientific data analysis, CRPQ optimization lacks a unified framework, relying inst...
Yue Pang, Lei Zou, Angela Bonifati et al.· Proceedings of the VLDB Endo...· 0 citations
LiteRAG is presented, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction and attains the highest overall quality among the evaluated methods.
Daniel Alejandro Coll Tejeda, Pedro García López, Daniel Barcelona-Pons· 0 citations
guided table retrieval is presented, a four-phase pipeline that combines deterministic grounding via hash-based predictors, structural exploration of join-graph reachability, LLM-powered disambiguation of sources and targets, and algorithmic merging into minimal, topologically ordered join trees.
Alekh Jindal, J. Pandey, C. Pavlopoulou et al.· 0 citations
DualLane, a novel parallel dual-path planning architecture designed for AI agents, adaptively manages the highly skewed frequency distribution characteristic of real-world user queries and underscores the practical viability and effectiveness of adaptive dual-path planning in large-scale, interactive AIOps systems.
Haoyu Wang, Wenxuan Ma, Bing Hu et al.· Proceedings of the 32nd ACM...· 0 citations
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