arXiv:2606. 15393v1 Announce Type: cross Abstract: Scientific discovery relies on large-scale hypothesis testing.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
arXiv:2605. 20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold.
arXiv:2511. 06701v3 Announce Type: replace-cross Abstract: AI-Scientist systems risk manufacturing spurious discoveries through uncontrolled multiple testing.
arXiv:2606. 11851v1 Announce Type: new Abstract: Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions.
arXiv:2602. 06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencies due to adherence to fixed initial priors.
The paper investigates how closed‑loop autonomous discovery systems can develop false‑science induction when physical objects and measurements are incorrectly paired. It demonstrates that such misbinding causes neural surrogates to learn spurious associations, diverting experimental effort toward low‑performing regions in both green fluorescent protein fitness and materials band‑gap prediction loops. The study shows that the coherence of these errors—not just their frequency—drives budget misallocation and proposes monitoring strategies to detect and quarantine corrupted hypothesis axes.