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Back to the Future: A workbook time machine for spread sheet creation benchmarks

arXiv 2608.07873 ↗First posted 8 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

We introduce the workbook time machine, a pipeline that automatically creates benchmarks evaluating the ability of language models to create derived objects in spreadsheets (formulas, charts, pivot tables, and conditional formatting). Applied to public workbook corpora, it produces wtmcorpus--a collection of (input workbook, output workbook, query) triples spanning four artifact types and varying complexity. From this corpus we curate wtmbench, a 150-task evaluation benchmark with queries at three levels of specificity. We evaluate existing spreadsheet manipulation agents and baselines on wtmbench across artifact types, step complexity, and instruction granularity. Our evaluations show that query specificity, agent orchestration, and interface API used to control spreadsheets play a big role in LLM performance on Excel tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 8 Aug 2026.

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References · 7

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Teaching Large Language Models to Self-Debugcited by 988
  3. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 585
  4. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  5. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 79
  6. 2024SpreadsheetLLM: Encoding Spreadsheets for Large Language Modelscited by 17
  7. 2023InstructExcel: A Benchmark for Natural Language Instruction in Excelcited by 15

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