Answers are grounded in this paper and cite their sources.
lab-gate is an open-source plan-checking safety gate for autonomous laboratories, built on the public helao-async framework and evaluated inside its OER electrocatalysis simulator (OERSIM). Before a plan runs, the gate checks it against a registry of written invariants and returns PASS with a proof certificate, BLOCK with a counterexample, or ABSTAIN routed to a human. No decision path reads free-text fields. This report presents a preregistered adversarial evaluation of the gate against two baselines a lab would actually reach for: frontier language-model gates (two model families) and a trained classifier, attacked with static, decomposition, and adaptive attackers, scored against a simulator ground-truth oracle. The typed gate let through no in-specification attack and never false-blocked a benign plan, including a surprise subset of unusual-but-valid plans; the language-model gates were unreliable in opposite directions; the classifier degraded under adaptive attack. The evaluation also caught and corrected a measurement artifact in one of its own baseline arms, documented in a corrections file. All results are reproducible offline from cached model responses, with every number rendered from a locked registry. Code and data: https://github.com/Anuja-Khatri/lab-gatePreregistration (binding timestamp is the public git tag prereg-v1, commit b06ae95, 2026-11-03): https://osf.io/jxhfeAI disclosure: this project was carried out by Claude Code from a specification written with Claude, with the author reviewing and confirming the decision rules.
No takes yet. Share an insight, caveat, or question.
Anuja Khatri (2026) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: