CrossSWE: Benchmarking Cross-Repository Joint Development for Coding Agents
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabCrossSWE is a benchmark for cross-repository joint development by coding agents, comprising 171 real-world instances spanning 165 repositories, 20 technical domains, and 13 programming languages. It checks whether coordinated changes are necessary by testing whether partial repository modifications fail to achieve intended behavior. Evaluation of five LLMs found the strongest, GPT-6.1 Sol, reached 58.48% Pass@1, with cross-repository inconsistencies as the most common failure mode at 44.2%.
One primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
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CrossSWE is a benchmark for cross-repository joint development by coding agents, comprising 171 real-world instances spanning 165 repositories, 20 technical domains, and 13 programming languages. It checks whether coordinated changes are necessary by testing whether partial repository modifications fail to achieve intended behavior. Evaluation of five LLMs found the strongest, GPT-6.1 Sol, reached 58.48% Pass@1, with cross-repository inconsistencies as the most common failure mode at 44.2%.
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- 01Who is making each claim?
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- 02What was actually tested?
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- 03Where are the sources?
Original link: huggingface.co. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; the supplied text is an abstract and community post, without full methodology or evaluation details.
- 05What is at risk if wrong?
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- 06What would change the view?
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Key findings from the stored brief
- The benchmark contains 171 real-world instances spanning 165 repositories, 20 technical domains, and 13 programming languages.
- Partial repository modifications are tested to verify that coordinated cross-repository changes are necessary.
- The strongest of five evaluated LLMs, GPT-6.1 Sol, achieved 58.48% Pass@1.
- Cross-repository inconsistencies were the most prevalent failure mode, at 44.2%.
Source claims not independently checked; the supplied text is an abstract and community post, without full methodology or evaluation details.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Related archive coverage
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The paper page is linked. Peer review and findings have not been independently checked by AINewsIQ.
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AINewsIQ prepared one abstract from fetched source page.
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Why this may matter
Real-world software often spans interdependent repositories, so benchmarks limited to single repositories may not capture dependency reasoning, change propagation, and interface consistency challenges that coding agents face in practice.
Limits and open questions
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