The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents
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In the citing paper
“SWE-rebench (Badertdinov et al., 2025) introduces a continual GitHub-mining pipeline (>21k tasks) for both training and evaluation.”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Badertdinov et al., 2025
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Citations
Cites
Measuring Coding Challenge Competence With APPS
Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2105.09938 · Cited by 142
Cited by
The landscape of agentic reinforcement learning for LLMs: a survey
Zhang, G., Geng, H., Yu, X., Yin, Z., Zhang, Z., Tan, Z., Zhou, H., Li, Z., Xue, X., Li, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.02547 · Cited by 229
ProgramBench: Can Language Models Rebuild Programs From Scratch?
Yang, J., Lieret, K., Ma, J., Thakkar, P., Pedchenko, D., Sootla, S., McMilin, E., Yin, P., Hou, R., Synnaeve, G., et al. (2026) · arXiv · doi:10.48550/arxiv.2605.03546 · Cited by 84
SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
Le, T., Thai, M. V. T., Manh, D. N., Nhat, H. P., & Bui, N. D. Q. (2025) · arXiv · doi:10.48550/arxiv.2512.18470 · Cited by 79
Context as a Tool: Context Management for Long-Horizon SWE-Agents
Liu, S., Yang, J., Jiang, B., Li, Y., Guo, J., Liu, X., & Dai, B. (2025) · arXiv · doi:10.48550/arxiv.2512.22087 · Cited by 35
VERSE: Verified Self-Evolving Optimizer for Agent Harnesses
Wang, Z., Ge, Y., Wang, Z., Wang, H., Xu, Y., Frandsen, J., Fu, S., Wilson, A. C., & Reddy, C. K. (2026) · arXiv · doi:10.48550/arxiv.2610.02616
Metadata
- Authors
- Badertdinov, I., Golubev, A., Nekrashevich, M., Shevtsov, A., Karasik, S., Andriushchenko, A., Trofimova, M., Litvintseva, D., & Yangel, B.
- Affiliations
- Nebius
- Nebius AI
- Yandex
- Venue
- Advances in Neural Information Processing Systems 38
- Published
- 2025
- Pages
- 26420-26466
- Cited by
- 14
- References
- 1
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
