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Published directly by arXiv.
Reproducible and analytically robust research findings are essential to scientific progress, but evaluating these properties typically requires costly manual effort. We investigate whether LLM agents can help automate reproduction and robustness analysis, using a medical case study grounded in the Surveillance, Epidemiology, and End Results (SEER) database, a widely used U.S. cancer registry underlying tens of thousands of published studies. First, we introduce SEER-Repro, a benchmark of 20 published SEER-based papers together with the data and resources required to reproduce their findings.…
Not summarised — no summariser is configured, so the text above is the source's own excerpt.
| Age | -12.3 |
| Primary source | 20 |
| Total | 7.7 |
These components add up to 7.699999999999999, not 7.7. The total is what ordered your brief; the difference means a component was not recorded, so treat this breakdown as incomplete rather than as the whole reason.