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- claim-1-the-swe-fficiency-benchmark-comprises-498-real-world-code-optimization-tasks-drawn-from-9-widely-used-python-repositories-including-numpy-pandas-and-scipy-abstract-only
- claim-2-agents-are-tasked-with-investigating-code-semantics-localizing-bottlenecks-and-relevant-tests-and-producing-a-patch-that-matches-or-exceeds-expert-speedup-while-passing-the-same-unit-tests-abstract-only
- claim-3-the-top-performing-agents-evaluated-on-swe-fficiency-achieve-less-than-0-23x-of-the-expert-level-speedup-on-average-across-the-498-tasks-abstract-only
- claim-4-agents-struggle-specifically-with-localizing-optimization-opportunities-in-large-codebases-reasoning-about-execution-flow-across-multiple-functions-and-maintaining-correctness-of-proposed-optimizations-abstract-only
- conclusion
- executive-summary
- 1.81 kB