arXiv AI By Yuxin Zhang, Ju Fan, Meihao Fan, Shaolei Zhang, Xiaoyong Du

CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?

Read the original on arXiv AI →

arXiv:2606. 15300v1 Announce Type: new Abstract: Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
Jun 30

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.

By Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan
arXiv AI
Aug 12

DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?

arXiv:2608. 10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments.

By Mizanur Rahman, Mohammed Saidul Islam, Ridwan Mahbub, Md Tahmid Rahman Laskar, Shafiq Joty, Enamul Hoque Prince