Orateur
Description
Modern scientific facilities increasingly combine complex instrumentation, simulations, heterogeneous diagnostics, and machine-learning-based control. We propose MODULARITY, a domain-agnostic architecture for distributed closed-loop experimentation, where complex systems are decomposed into interoperable autonomous modules. Each module combines observations, control variables, constraints, a local digital twin, and quantified uncertainty. Local predictions and measurements are continuously aggregated into Global Maps, dynamic representations of the experimentally accessible space containing current performance, explored and unexplored regions, model uncertainty, physical constraints, and potentially scientific novelty. These maps provide the common information layer through which higher-level agents coordinate and guide local optimization, rather than relying on a single monolithic model or controller \cite{tennant2026autonomous,brynes2026closing,kelly2026bayesian,hickman2025atlas}.
The ML layer combines probabilistic surrogate modelling, Bayesian inference, Bayesian optimization, reinforcement learning, and decentralized or multi-agent optimization. Local agents perform data-efficient control and adaptation, while Global Maps act as an evolving optimization and exploration landscape, identifying where additional measurements or actions are expected to provide the greatest operational or scientific value. The coordination layer can therefore allocate optimization effort across modules according to predicted gain, uncertainty, constraints, and cross-module dependencies. Beyond performance optimization, MODULARITY uses Bayesian experimental design to guide scientific inference, selecting experiments that maximize expected information gain,
$$ d^\star=\arg\max_d I(\theta;Y\mid d,D), $$ or discriminate between competing models through $I(M;Y\mid d,D)$ \cite{rainforth2024bed}. Global Maps can consequently evolve from simple performance landscapes into probabilistic knowledge maps combining model predictions, uncertainty, experimental coverage, and evidence for model inadequacy.
At the discovery level, MODULARITY distinguishes parameter inference, model discrimination, persistent model inadequacy, and open-world novelty. Unexpected observations update the Global Maps and can redirect the autonomous exploration toward regions where competing models diverge, uncertainty is large, or observations systematically disagree with predictions. Such regions trigger targeted replication, diagnostic cross-checks, nuisance-parameter inference, and experiments designed to discriminate between alternative explanations. Scientific value can therefore be represented through a multi-objective discovery utility combining information gain, falsification power, predictive surprise, reproducibility, novelty, cost, and risk. Domain-specific modules define the relevant likelihoods, priors, observables, and evidential criteria, while the common Global Map and coordination framework enables applications ranging from particle accelerators and particle physics to materials science.
References
\cite{rainforth2024bed}
T. Rainforth, A. Foster, D. R. Ivanova, and F. B. Smith, “Modern Bayesian Experimental Design,” Statistical Science 39(1), 100–114 (2024).
\cite{hickman2025atlas}
R. J. Hickman et al., “Atlas: a brain for self-driving laboratories,” Digital Discovery 4, 1006–1029 (2025).
\cite{tennant2026autonomous}
C. Tennant, “Toward a Fully Autonomous, AI-Native Particle Accelerator,” arXiv:2602.17536 (2026).
\cite{brynes2026closing}
A. D. Brynes et al., “Closing the Loop: Deploying Auto-Generating Digital Twins for Particle Accelerators,” arXiv:2604.19101 (2026).
\cite{kelly2026bayesian}
C. Kelly et al., “Machine Learning Assisted Bayesian Calibration of an Accelerator Digital Twin from Orbit Response Data,” arXiv:2608.17820 (2026).