--- title: [2605.03546] ProgramBench: Can Language Models Rebuild... description: ProgramBench is a new benchmark evaluating whether LLM-based software engineering agents can rebuild entire programs from scratch given only a reference... canonical: https://daily.dev/posts/2605-03546-programbench-can-language-models-rebuild-programs-from-scratch--pnyvfwifh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: [2605.03546] ProgramBench: Can Language Models Rebuild Programs From Scratch? | daily.dev og:description: ProgramBench is a new benchmark evaluating whether LLM-based software engineering agents can rebuild entire programs from scratch given only a reference... og:url: https://daily.dev/posts/2605-03546-programbench-can-language-models-rebuild-programs-from-scratch--pnyvfwifh og:image: https://api.daily.dev/og/posts/pNyvFwIfh.png og:image:alt: [2605.03546] ProgramBench: Can Language Models Rebuild Programs From Scratch? og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # [2605.03546] ProgramBench: Can Language Models Rebuild Programs From Scratch? **[Hacker News](https://daily.dev/sources/hn)** · 2 min read · 0 upvotes · 0 comments ## Summary ProgramBench is a new benchmark evaluating whether LLM-based software engineering agents can rebuild entire programs from scratch given only a reference executable and its documentation. Unlike existing benchmarks that focus on narrow tasks like bug fixes or single features, ProgramBench requires agents to make holistic software architecture decisions. The 200 tasks span compact CLI tools to major projects like FFmpeg, SQLite, and the PHP interpreter. Behavioral tests are generated via agent-driven fuzzing without prescribing implementation structure. Evaluating 9 language models, none fully resolved any task — the best model passed 95% of tests on only 3% of tasks. A notable finding is that models tend to produce monolithic single-file implementations that diverge significantly from human-written code. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Most coding agent benchmarks skip large-scale refactoring. 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