# Coordinated pausing: An evaluation-based coordination scheme for frontier AI developers

arXiv: https://arxiv.org/abs/2310.00374 · HTML: https://mlsanity.com/paper/2310.00374/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2023.

## Built on by (most cited first, 13 of 13)

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- [An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease Phenotyping](https://mlsanity.com/paper/2311.08687/) (2023)
- [Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations](https://mlsanity.com/paper/2412.15433/) (2024)
- [International Agreements to Limit Frontier AI: Objectives and Exit](https://mlsanity.com/paper/2607.16224/) (2026)
- [A three-dimensional typology of agency for advanced AI systems](https://mlsanity.com/paper/2608.20041/) (2026)
- [Comprehension Audits to Mitigate Risks from Automated AI Research](https://mlsanity.com/paper/2610.10064/) (2026)

## Abstract (arXiv)

As artificial intelligence (AI) models are scaled up, new capabilities can emerge unintentionally and unpredictably, some of which might be dangerous. In response, dangerous capabilities evaluations have emerged as a new risk assessment tool. But what should frontier AI developers do if sufficiently dangerous capabilities are in fact discovered? This paper focuses on one possible response: coordinated pausing. It proposes an evaluation-based coordination scheme that consists of five main steps: (1) Frontier AI models are evaluated for dangerous capabilities. (2) Whenever, and each time, a model fails a set of evaluations, the developer pauses certain research and development activities. (3) Other developers are notified whenever a model with dangerous capabilities has been discovered. They also pause related research and development activities. (4) The discovered capabilities are analyzed and adequate safety precautions are put in place. (5) Developers only resume their paused activities if certain safety thresholds are reached. The paper also discusses four concrete versions of that scheme. In the first version, pausing is completely voluntary and relies on public pressure on developers. In the second version, participating developers collectively agree to pause under certain conditions. In the third version, a single auditor evaluates models of multiple developers who agree to pause if any model fails a set of evaluations. In the fourth version, developers are legally required to run evaluations and pause if dangerous capabilities are discovered. Finally, the paper discusses the desirability and feasibility of our proposed coordination scheme. It concludes that coordinated pausing is a promising mechanism for tackling emerging risks from frontier AI models. However, a number of practical and legal obstacles need to be overcome, especially how to avoid violations of antitrust law.