ArticlePhysical review research
Curiosity-driven search for novel nonequilibrium behaviors.
Article in Physical review research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Discovering sensorimotor agency in cellular automata using diversity search.Science advances · 2025Article
- AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.eLife · 2025Article
- Chemical waves in reaction-diffusion networks of small organic molecules.Chemical science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Exploring the full spectrum of novel behaviors that a system can produce can be an intensive task. Sampling techniques developed in response to this exploration challenge often require a predefined metric, such as distance in a space of known order parameters. However, order parameters are rarely known for nonequilibrium systems, especially in the absence of a diverse set of example behaviors, creating a chicken-and-egg problem. Here, we combine active and unsupervised learning for automated exploration of nonequilibrium systems with unknown order parameters. We iteratively use active learning based on current order parameters to expand the library of behaviors and relearn order parameters based on this expanded library. We demonstrate the utility of this approach in Kuramoto models of increasing complexity. In addition to reproducing known phases, we reveal previously unknown behavior and related order parameters, and we demonstrate how to align search with human intuition.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.