Author

Chloe Bi

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#artificial intelligence Preprint Sep 2026

E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch

E2E-SWE is introduced, a benchmark for evaluating whether coding agents can build complete, functional software repositories end to end and finds substantial variation in end-to-end repository generation ability, providing strong model differentiation while leaving considerable headroom for future progress.

Hantian Ding, Chloe Bi, Jia-Cheng Zhu et al. · 0 citations

Preprint Aug 2026

Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+

Evaluating nine open-weight and commercial LLMs, it is found that no open-weight model covers the EU-24 well: in every official language both commercial systems outscore every open-weight model, and the two weakest average below 40 points across the EU-24.

Sherzod Hakimov, Karl Osswald, Eszter Jelle Psurek et al. · 0 citations