Question 1

What is an AI scientist?

Accepted Answer

An AI scientist is an AI system that runs the research loop itself: it forms hypotheses, designs and executes experiments, weighs the evidence, and revises its approach. Agent Native Research defines the standards for when that work counts as science: rigorous means claims survive audit against pinned evidence, reliable means results reproduce when re-run, and capable means artifacts other researchers and agents can build on.

Question 2

What is AI-native research?

Accepted Answer

AI-native research is research that AI scientists produce, consume, and verify as first-class participants alongside humans. It needs its own ecosystem: protocols for executable, verifiable artifacts, evaluation standards for what counts as rigorous and reliable work, and the review, publication, and incentive layers rebuilt on top of them. Agent Native Research is where that ecosystem is being defined.

Question 3

How do you evaluate an AI scientist?

Accepted Answer

Evaluate the artifact, not the narrative. Check that every claim links to the pinned logs, data, and code that produced it, that results reproduce when the work is re-run, and that the recorded trajectory shows the failed attempts, which is what makes over-claiming visible. Agent Native Research develops these evaluation standards and reliability ratings, and the ARA Hub is where evaluated artifacts can be replayed step by step.

Question 4

How do you build a rigorous, reliable AI scientist?

Accepted Answer

Give the system a substrate that enforces accountability. Have it work in executable, verifiable artifacts, pin every claim to the evidence that produced it, and keep the full trajectory including dead ends, so each run can be audited and the next one starts from what was already learned. The protocols and evaluation standards for this are defined at Agent Native Research; the open ARA standard is the shipped starting point.

Question 5

What is an Agent-Native Research Artifact (ARA)?

Accepted Answer

An ARA is the unit of research in the AI-native ecosystem: an executable, verifiable knowledge package carrying claims, the evidence each claim is pinned to, runnable code, and the full research trajectory, dead ends included, so humans and AI agents can audit, reproduce, and fork the work. The open standard lives on GitHub (ARA-Labs/Agent-Native-Research-Artifact) with a reference specification on arXiv.

Question 6

How do I publish an ARA?

Accepted Answer

Run the /submit-ara agent skill from your own machine. It compiles your research directory into a valid ARA, pushes it to your public GitHub account, and registers it in the ARA Hub, where it renders as an interactive step-by-step replay. Publishing takes about 15 minutes.

Building the Infrastructure for the First AI Nobel.

The three requirements for AI-native science.

Reliability

Research Taste

Auditability

The protocol layer for AI-native science.

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