ForgeVLA enables federated VLA model training from unlabeled vision-action pairs by recovering language via embodied classifiers and using contrastive planning plus adaptive aggregation to avoid feature collapse.
The rise of self-driving labs in chemical and materials sciences.Nature Synthesis, 2:483–492, 2023
6 Pith papers cite this work, alongside 506 external citations. Polarity classification is still indexing.
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2026 6representative citing papers
Pyomo.DoE gains callback-based support for eigenvalue optimality criteria (E- and ME-optimality) plus a new experiment-creation abstraction for uncertainty quantification in high-fidelity models.
Autonomous controllers cannot remove decision risk from target components absent from their pre-action physical records; closing that gap requires expanded access, cost, or restricted deployment.
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
ARES OS 2.0 is an open-source orchestration suite that enables closed-loop autonomous experimentation through central control, UI tools, data management, and user-creatable modules communicating via protobuf and gRPC.
BASIL is a GUI application implementing Bayesian optimization for single- and multi-objective process optimization using surrogate models.
citing papers explorer
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ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
ForgeVLA enables federated VLA model training from unlabeled vision-action pairs by recovering language via embodied classifiers and using contrastive planning plus adaptive aggregation to avoid feature collapse.
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Optimal Experimental Design using Eigenvalue-Based Criteria with Pyomo.DoE
Pyomo.DoE gains callback-based support for eigenvalue optimality criteria (E- and ME-optimality) plus a new experiment-creation abstraction for uncertainty quantification in high-fidelity models.
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Measurement-Access Risk Frontiers for Autonomous Scientific Control
Autonomous controllers cannot remove decision risk from target components absent from their pre-action physical records; closing that gap requires expanded access, cost, or restricted deployment.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
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ARES OS 2.0: An Orchestration Software Suite for Autonomous Experimentation Systems and Self-Driving Labs
ARES OS 2.0 is an open-source orchestration suite that enables closed-loop autonomous experimentation through central control, UI tools, data management, and user-creatable modules communicating via protobuf and gRPC.
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BASIL: Bayesian Application for Scientific Iteration and Learning
BASIL is a GUI application implementing Bayesian optimization for single- and multi-objective process optimization using surrogate models.