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This work proposes a trajectory-aware subset selection approach that replaces random sampling with deterministic selection based on trajectory embeddings, and shows that a 10% trajectory-aware subset keeps the median estimation error below 5% while cutting token cost by roughly 90%.
Seemingly impressive results on benchmarks like SWE-bench may be inflated by weak test suites that accept incorrect patches, with a new analysis showing top repair agents' performance dropping by up to 9% when evaluated against more robust tests.
