Sheet-Raptor: Reconstructing Context for Spreadsheet Reasoning

Spreadsheets are widely used in real-world data analysis. However, their multi-sheet organization and irregular table structures require understanding both structure and semantics within and across files, which conventional database techniques do not adequately support. Existing LLM-, RAG-, and agent-based methods struggle with lengthy and redundant spreadsheet content, fragmented context from row-level retrieval, and limited flexibility across analysis tasks. To address these challenges, we propose Sheet-Raptor, a unified system for context-critical spreadsheet analysis. First, it combines structure-aware preprocessing with layout-sensitive folding to reduce redundancy while preserving semantically meaningful table regions. Second, it integrates multi-granular spreadsheet embeddings, question rewriting, and progressive agentic retrieval to recover relevant context across files. Third, it decomposes queries into spreadsheet-specific atomic operations and dynamically coordinates semantic reasoning and programmatic execution. Experiments on four benchmarks show that Sheet-Raptor consistently outperforms state-of-the-art baselines, improving accuracy by up to 18.74%.