SheetCompass: Hierarchical Relation Graphs for Agentic Spreadsheet Reasoning

Abstract:Spreadsheets are widely used to organize, analyze, and manipulate semi-structured data, yet automated spreadsheet reasoning remains challenging for large language models (LLMs). Real-world workbooks often contain implicit cross-table associations, fine-grained column dependencies, and complex spatial layouts. Existing methods typically flatten these multidimensional structures into sequential strings, losing important intra-sheet boundaries and inter-sheet semantics. Consequently, LLMs cannot exploit the global spatial context that human experts naturally use when inspecting spreadsheets. We propose SheetCompass, a graph-guided and memory-driven agentic framework for spreadsheet reasoning and automation. SheetCompass explicitly models structural relationships within and across worksheets while maintaining task-relevant information in memory, enabling agents to reason more effectively over complex workbooks.

Artificial Intelligence (cs.AI)

arXiv:2608.14452 [cs.AI]

(or arXiv:2608.14452v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.14452 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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