[{"data":1,"prerenderedAt":24},["ShallowReactive",2],{"blog-post-en-US-musework-tablebench-table-analysis-ranking":3},{"id":4,"slug":5,"title":6,"seo_title":7,"seo_description":8,"summary":9,"cover_alt":10,"author":11,"category":12,"tags":13,"cover_url":17,"cover_width":18,"cover_height":19,"is_pinned":20,"is_featured":21,"published_at":22,"modified_at":22,"content":23},"1791417600000-musework-tablebench-table-analysis-ranking","musework-tablebench-table-analysis-ranking","MuseWork’s table analysis ranks first on TableBench’s Methodology leaderboard","MuseWork Tops TableBench’s Methodology Leaderboard for Table Analysis","MUSE PULSE, the table-analysis capability in MuseWork, ranks #1 on TableBench’s Methodology leaderboard with 80.64. See what was tested and try a better question on your own data.","What MuseWork’s TableBench result tells us about turning spreadsheet data into answers a team can use.","MUSE PULSE ranks first on TableBench’s Methodology leaderboard with an overall score of 80.64 and scores for fact checking, numerical reasoning, data analysis, and visualization.","MuseWork Content Team","Product",[14,15,16],"TableBench","MUSE PULSE","MuseWork","/images/musework-tablebench-complete-cover.webp",2048,1152,false,true,1791417600,"A spreadsheet can show that revenue changed. It takes more work to tell your team where the change came from: which weeks are being compared, whether returns count, and which region moved the total. TableBench tests the reasoning behind questions like these.\n\nOn **October 8, 2026**, **MUSE PULSE**, the table-analysis capability available to MuseWork users, ranked **#1 on [TableBench’s Methodology leaderboard](https://tablebench.github.io/)** with an **overall score of 80.64**. Here’s what the benchmark asks, what stands out in our results, and how to ask better questions of your own data.\n\n![TableBench Methodology top 10 on October 8, 2026: MUSE PULSE ranks first with an overall score of 80.64, alongside category scores and a human-performance reference.](/images/musework-tablebench-methodology-top-10.webp)\n\n## Why this is a demanding test of table questions\n\nTableBench goes beyond looking up a value in one cell. Researchers assembled **886 test cases across 18 types of question**, grouped under fact checking, numerical reasoning, data analysis, and visualization. According to the paper, the questions require **6.26 reasoning steps on average**—a measure of their complexity.\n\nThe researchers designed it to get closer to the kind of table questions that arise at work, where an answer may require checking several entries or calculating an intermediate figure. The official site provides the paper, test data, evaluation code, and **two separate leaderboards: Methodology and Large Language Models**. Our result appears on **Methodology**; the two tracks should be read by their own labels. The project also updated its test set and refined several scoring metrics in April 2025, so the current leaderboard—not a score quoted from the original paper—is the place to verify today’s ranking.\n\nThe differences between the question types are useful in everyday work:\n\n- **Fact checking:** A note says a product’s sales rose in every quarter. Does the table actually support that claim across the relevant periods?\n- **Numerical reasoning:** The answer is not printed in one cell. You must select the right rows, aggregate them, and calculate a comparison or rate.\n- **Data analysis:** A team needs to describe a pattern, spot an unusual change, or assess a trend. That takes a clearer question than “analyze this spreadsheet.”\n- **Visualization:** The finding needs a chart that communicates the comparison. The benchmark’s chart task checks whether a correct chart is generated on the first attempt.\n\nThe official evaluation guide scores fact checking and numerical reasoning by **exact match**. Data-analysis questions use different metrics depending on the task: an impact question is checked differently from a forecast or an open-ended explanation. Visualization uses **Pass@1**, asking whether the chart is right on the first attempt. That’s why the category breakdown tells you more than the overall score alone.\n\n## What MUSE PULSE scored—and what to read into it\n\n| TableBench category | MUSE PULSE score | A work question it resembles |\n| --- | ---: | --- |\n| Fact checking | **91.67** | Is the statement in the update supported by the table? |\n| Numerical reasoning | **93.70** | What is the change after the correct rows and periods are counted? |\n| Data analysis | **74.04** | Which pattern deserves attention, and how should it be described? |\n| Visualization | **88.0** | Can the result be communicated in a chart? |\n| **Overall** | **80.64** | **Reported aggregate on the Methodology leaderboard** |\n\nThe fact-checking and numerical-reasoning scores are especially relevant to weekly updates and business reviews: if the figures or the comparison are wrong, even a well-written explanation falls apart. Data analysis scores lower than the other three areas, showing us where there’s more to improve. TableBench also lists a **human-performance reference score of 85.91**. The #1 ranking is for table questions on its Methodology leaderboard.\n\n## Turn one spreadsheet question into a piece of work\n\nSay you’re preparing a Friday sales update from a table of weeks, regions, product lines, revenue, and returns. Rather than asking for a general analysis, tell MuseWork what your manager needs to know:\n\n> “Using this weekly sales table, compare the latest complete week with the week before it. Show which region and product line contributed most to the change in net sales. State whether returns are included, show the figures behind the comparison, and write three points for a leadership update. Suggest a chart only if it makes the change easier to understand.”\n\nWhat makes the request useful is its specificity: **time period, metric, breakdown, calculation rules, supporting figures, and audience**. Check the selected rows, the definition of net sales, and the calculations against your source. Then keep going in the same task—tighten the explanation, look at a different segment, or prepare a version for another audience.\n\nThat’s the work MuseWork is built to help with as **your professional AI agent**: not just finding a number, but turning the materials you already have into a result you can review, share, and build on.\n\n**Have a spreadsheet open? [Start with one question in MuseWork](https://museai.im/en-US/chat?scene=standard).** The comparison you need for your next update is a good place to begin.\n\n## Prefer to work with files on your computer?\n\nIf the files are on your computer, [download MuseWork Desktop](https://museai.im/en-US/download) for macOS, Windows, or Linux. With your permission, you can bring the local files you choose into the task. Prefer a browser? You can start on the web too.\n\n### Sources\n\n- [TableBench leaderboard and benchmark overview](https://tablebench.github.io/)\n- [TableBench paper: task design and dataset statistics](https://arxiv.org/abs/2408.09174)\n- [TableBench evaluation metrics](https://github.com/TableBench/TableBench#-evaluation-metrics)",1791460696059]