The pursuit of reproducible findings underpins credible scientific progress, yet the manual effort required to verify claims across large corpora of research has grown proportionally with publication volume. SEER-Driven Reproducibility and Analytical Robustness in Scientific Discovery investigates whether large language model agents can automate portions of this verification pipeline. The authors construct SEER-Repro, a benchmark comprising 20 published papers that rely on the Surveillance, Epidemiology, and End Results (SEER) cancer registry, accompanied by the data and metadata necessary to reproduce each study's core claims. This benchmark enables systematic assessment of an agent's ability to re-derive published results from scratch.
An LLM agent was deployed to reproduce atomic claims extracted from the SEER-Repro corpus. Across 2,190 claims, the agent successfully reproduced 77.4% of cases, while generating executable baselines that downstream researchers can extend or modify. The reproduction process entailed retrieving the original dataset, reconstructing analytic workflows from narrative descriptions in the source papers, and running the agent's code against the SEER database. In instances where the agent encountered ambiguous or underspecified steps, it produced shell scripts and Python notebooks that documented its assumptions, allowing human reviewers to validate or correct the trajectory.
Beyond reproduction, the paper introduces an agentic analysis pipeline designed to probe the robustness of each finding. For every fully reproduced result, the pipeline enumerates the analytical choices that underpin the conclusion—such as variable codings, inclusion criteria, statistical model specifications, and normalization procedures. For each choice, the agent proposes a defensible alternative, reruns the analysis with the alternative specification, and records whether the conclusion shifts. Applied across the SEER-Repro benchmark, the pipeline flagged potentially consequential analytical choices in 9.5% of fully reproduced findings, marking them for expert review. The low but nontrivial fraction suggests that a subset of published results are sensitive to modeling decisions that are not always transparently documented.
The authors emphasize that the agent's role is not to supplant expert review but to surface regions of the evidence base where documentation gaps or modeling flexibility may obscure the stability of a conclusion. By automating the extraction and execution of analytical pathways, the pipeline reduces the time and expertise required for initial robustness checks, directing human attention to the cases most likely to benefit from deeper scrutiny.
From a practical standpoint, the work offers a template for institutions seeking to maintain rigorous review standards amid growing publication volumes. The SEER-Repro benchmark and the agentic pipeline are released to facilitate replication by other research groups. The authors note that the approach generalizes to other data-intensive domains beyond cancer epidemiology, provided that sufficient metadata and data descriptors are available for the agent to reconstruct workflows.
In summary, this study demonstrates that LLM agents can meaningfully contribute to the reproducibility and robustness lifecycle of scientific research. By reproducing a large majority of atomic claims from a focused benchmark and systematically identifying analytical choices that may affect conclusions, the approach provides a scalable mechanism for directing expert review toward findings whose stability is uncertain. The released benchmark and code aim to lower the barrier for subsequent work on automated scientific review.