|
10 | 10 |
|
11 | 11 | version(); |
12 | 12 |
|
13 | | - const { title, description, url, keywords } = copy; |
14 | 13 | setContext("copy", copy); |
15 | 14 | setContext("data", data.data); |
| 15 | +
|
| 16 | + const title = |
| 17 | + "FormulaCode: LLM Agent Benchmark for Python Code Optimization"; |
| 18 | + const description = |
| 19 | + "FormulaCode is a continually updated benchmark evaluating LLM coding agents on real-world Python code optimization. 957 performance bottlenecks from 70+ scientific Python repositories, each paired with expert patches and community-maintained performance workloads. Accepted at ICML 2026."; |
| 20 | + const url = "https://formulacode.org/"; |
| 21 | +
|
| 22 | + const dataset = { |
| 23 | + "@context": "https://schema.org", |
| 24 | + "@type": "Dataset", |
| 25 | + name: "FormulaCode", |
| 26 | + alternateName: "FormulaCode Benchmark", |
| 27 | + description, |
| 28 | + url, |
| 29 | + identifier: "https://formulacode.org/", |
| 30 | + keywords: [ |
| 31 | + "code optimization", |
| 32 | + "LLM agents", |
| 33 | + "Python performance", |
| 34 | + "benchmark dataset", |
| 35 | + "agentic optimization", |
| 36 | + "software engineering benchmark" |
| 37 | + ], |
| 38 | + license: "https://opensource.org/licenses/MIT", |
| 39 | + creator: { |
| 40 | + "@type": "Organization", |
| 41 | + name: "FormulaCode", |
| 42 | + url: "https://formulacode.org/", |
| 43 | + sameAs: [ |
| 44 | + "https://github.com/formula-code", |
| 45 | + "https://huggingface.co/formulacode", |
| 46 | + "https://data.formulacode.org/", |
| 47 | + "https://api.formulacode.org/" |
| 48 | + ] |
| 49 | + }, |
| 50 | + distribution: [ |
| 51 | + { |
| 52 | + "@type": "DataDownload", |
| 53 | + name: "FormulaCode on Hugging Face", |
| 54 | + contentUrl: "https://huggingface.co/datasets/formulacode/formulacode-all", |
| 55 | + encodingFormat: "application/json" |
| 56 | + }, |
| 57 | + { |
| 58 | + "@type": "DataDownload", |
| 59 | + name: "FormulaCode live dashboard", |
| 60 | + contentUrl: "https://data.formulacode.org/", |
| 61 | + encodingFormat: "text/html" |
| 62 | + }, |
| 63 | + { |
| 64 | + "@type": "DataDownload", |
| 65 | + name: "fc-eval evaluation harness", |
| 66 | + contentUrl: "https://github.com/formula-code/fc-eval", |
| 67 | + encodingFormat: "application/x-git" |
| 68 | + } |
| 69 | + ], |
| 70 | + isAccessibleForFree: true, |
| 71 | + measurementTechnique: |
| 72 | + "Statistical comparison of expert and LLM-agent patches across multi-objective performance workloads on real-world Python repositories.", |
| 73 | + variableMeasured: [ |
| 74 | + "speedup ratio", |
| 75 | + "agent advantage", |
| 76 | + "stratified advantage" |
| 77 | + ] |
| 78 | + }; |
| 79 | +
|
| 80 | + const scholarlyArticle = { |
| 81 | + "@context": "https://schema.org", |
| 82 | + "@type": "ScholarlyArticle", |
| 83 | + headline: "FormulaCode: Evaluating Agentic Optimization on Large Codebases", |
| 84 | + name: "FormulaCode: Evaluating Agentic Optimization on Large Codebases", |
| 85 | + abstract: description, |
| 86 | + url, |
| 87 | + sameAs: "https://arxiv.org/abs/2603.16011", |
| 88 | + datePublished: "2026", |
| 89 | + inLanguage: "en", |
| 90 | + author: [ |
| 91 | + { |
| 92 | + "@type": "Person", |
| 93 | + name: "Atharva Sehgal", |
| 94 | + url: "https://atharvas.net/" |
| 95 | + }, |
| 96 | + { |
| 97 | + "@type": "Person", |
| 98 | + name: "James Hou", |
| 99 | + url: "https://jamesahou.github.io/" |
| 100 | + }, |
| 101 | + { |
| 102 | + "@type": "Person", |
| 103 | + name: "Akanksha Sarkar", |
| 104 | + url: "https://milstein-program.as.cornell.edu/akanksha-sarkar/" |
| 105 | + }, |
| 106 | + { |
| 107 | + "@type": "Person", |
| 108 | + name: "Ishaan Mantripragada", |
| 109 | + url: "https://www.linkedin.com/in/ishaanmantri/" |
| 110 | + }, |
| 111 | + { |
| 112 | + "@type": "Person", |
| 113 | + name: "Swarat Chaudhuri", |
| 114 | + url: "https://www.cs.utexas.edu/~swarat/" |
| 115 | + }, |
| 116 | + { |
| 117 | + "@type": "Person", |
| 118 | + name: "Jennifer J. Sun", |
| 119 | + url: "https://jenjsun.com/" |
| 120 | + }, |
| 121 | + { |
| 122 | + "@type": "Person", |
| 123 | + name: "Yisong Yue", |
| 124 | + url: "https://www.yisongyue.com/" |
| 125 | + } |
| 126 | + ], |
| 127 | + publisher: { |
| 128 | + "@type": "Organization", |
| 129 | + name: "International Conference on Machine Learning (ICML)", |
| 130 | + url: "https://icml.cc/Conferences/2026" |
| 131 | + }, |
| 132 | + isPartOf: { |
| 133 | + "@type": "PublicationEvent", |
| 134 | + name: "ICML 2026", |
| 135 | + url: "https://icml.cc/Conferences/2026" |
| 136 | + } |
| 137 | + }; |
| 138 | +
|
| 139 | + const website = { |
| 140 | + "@context": "https://schema.org", |
| 141 | + "@type": "WebSite", |
| 142 | + name: "FormulaCode", |
| 143 | + url, |
| 144 | + description, |
| 145 | + publisher: { |
| 146 | + "@type": "Organization", |
| 147 | + name: "FormulaCode", |
| 148 | + url: "https://formulacode.org/" |
| 149 | + } |
| 150 | + }; |
| 151 | +
|
| 152 | + const jsonLd = [dataset, scholarlyArticle, website]; |
16 | 153 | </script> |
17 | 154 |
|
18 | | -<Meta {title} {description} {url} {keywords} /> |
| 155 | +<Meta {title} {description} {url} {jsonLd} /> |
19 | 156 | <OverviewPage leaderboardData={data.leaderboard} /> |
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