Scientists say their studies are reproducible, but can another lab actually run the same experiment from the paper alone?

This reads the question as whether one lab can pick up another lab's experimental recipe and run it. The corpus has one direct answer, and it says the barrier is being lowered, not that anyone has shown it cleared. Pairit, a tool for live human-AI experiments, targets the problem directly. Can declaring experiments in code make them reproducible?

The usual obstacle is that a live human-AI study runs on custom software, and the paper only describes it in prose. A methods section can't be executed. Pairit replaces the prose with one YAML file that spells out who the participants are, what role each AI plays, how messages get routed, and when things happen. That file can be audited, varied, and handed to another lab. The note describes this as making reproduction possible. It doesn't report a second lab actually running someone else's file, so the cross-lab claim is a design promise, not a tested result.

The agent-systems notes show how portability tends to work out. A survey of nine agent communication protocols finds a trilemma: rigid-schema protocols are efficient and portable but not versatile, and flexible ones are versatile but pay in negotiation overhead. Can agent protocols be efficient, versatile, and portable simultaneously? A declarative experiment file looks like the rigid-schema kind. It travels well because it only says what its schema can express. My inference is that a study needing something the schema can't express falls back to custom code, and that's where cross-lab handoff would break.

Two adoption findings point the same way. Coordination standards spread by wrapping the protocols people already use, not by replacing them. Should coordination protocols wrap existing systems or replace them? And in a survey of 306 practitioners, 85% of production teams build custom agents and skip shared frameworks, after protocol-mediated tool access proved non-deterministic. Why do protocol-based tool integrations fail in production workflows? These come from engineering teams, not academic labs, so they are analogies and not evidence about research culture. Still, they suggest a shared experiment format only spreads if it fits what labs already do. Until someone reports a real handoff between labs, nothing in this collection can say whether that happens.