arXiv:2607. 27651v2 Announce Type: replace Abstract: Learned rules select samples for follow-up measurements in high-throughput experiments.
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The paper introduces OPAL, a held‑out decision test that evaluates follow‑up measurement rules in biological screens by freezing a rule and assessing unnecessary measurement, coverage, and value after cost against pre‑defined archive‑specific criteria. Using a six‑rule Cell Painting battery, the authors show that a high‑value rule would re‑image 96.01% of the library with a 97.14% false‑activation upper bound, illustrating that predicted value alone cannot justify replacing a fixed plan. In development, a sparse Cell Painting rule reduced added‑well burden 18.2‑fold but had a false‑discovery bound above 35%, leading the fixed plan to remain; similar analyses for LINCS–LJP and CTRP highlighted the need for fallback strategies and the importance of separating optimization from evidence. "whyItMatters":"OPAL provides a systematic way to determine whether a new measurement strategy truly improves experimental efficiency without compromising data quality, as demonstrated across multiple biological screening datasets."
arXiv:2607. 27651v1 Announce Type: new Abstract: Adaptive laboratories choose measurements during experiments, yet most methods begin after adaptation is permitted.
SafeRestore introduces a framework for certifying when an industrial image restoration should be automatically returned to a detector or require human review. It ranks five restoration candidates using action‑specific fitted scores, selects a threshold gate on tuning data, and evaluates the gate on a separate certification sample with two one‑sided exact binomial bounds—one for evidence‑loss incidents and one for excess‑activation incidents. In a retrospective study of 4,591 Carinthia‑S images, the protocol demonstrates auditable risk‑coverage behavior, with varying pass rates across different policies and morphologies.
The paper introduces an admission‑audit protocol for continual embodied agents, arguing that update admission should balance error control with retained learning opportunities within a fixed interaction budget. It critiques a range‑based confidence gate for failing to certify unchanged old‑task behavior, and proposes a paired‑binomial construction that reduces this burden when outcome disagreements are rare. Experiments on a one‑step pushing diagnostic show that fresh paired checks admit a significant portion of updates while the range‑based gate admits none, and a learned‑dynamics stress test helps distinguish model bias from feedback‑selection error.
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
arXiv:2608. 14551v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty.