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. However, most benchmarks emphasize what to fix rather than how to fix code. We introduce SWE-fficiency, a benchmark for evaluating repository-level performance optimization on real workloads. Our suite contains 498 tasks across nine widely used data-science, machine-learning, and HPC repositories (e.g., numpy, pandas, scipy): given a complete codebase a
The rapid advancement of large language models (LLMs) has reached a point where their practical application in complex software optimization is being rigorously tested against real-world scenarios.
Evaluating LLMs on repository-level performance optimization with real workloads addresses a critical gap in current benchmarks, highlighting their potential to revolutionize software engineering productivity and efficiency.
The introduction of SWE-fficiency moves the assessment of AI in software development from theoretical capabilities to practical, measurable, and impactful optimization of existing, large-scale codebases.
- · Software developers
- · Hyperscalers
- · AI model developers
- · Data science platforms
- · Traditional software optimization tools
- · Manual optimization services
AI models will become increasingly adept at autonomous code optimization, reducing human effort in performance tuning.
The economic value of software repositories will be enhanced through AI-driven efficiency gains, leading to superior resource utilization and faster product cycles.
This could lead to a significant acceleration in the development and deployment of complex AI and HPC systems, as the underlying software can be continuously optimized by AI itself.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI