The paper introduces the Worst-case Marginal Benefit (WMB) criterion for sample-size design in test-and-roll experiments and shows it yields an optimal m approximately equal to N/3 for Bernoulli and Gaussian outcomes.
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SAGA reduces AI agent task completion time by 1.64x on 64-GPU clusters by scheduling at the full workflow level with execution graphs, affinity batching, and completion-time fairness.
A classical agent extracts more work from quantum temporal correlations via adaptive strategies bounded by the new Time-Ordered Free Energy, while reinforcement learning achieves polylogarithmic dissipation when learning unknown states.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
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Prior-Free Sample Size Design for Test-and-Roll Experiments
The paper introduces the Worst-case Marginal Benefit (WMB) criterion for sample-size design in test-and-roll experiments and shows it yields an optimal m approximately equal to N/3 for Bernoulli and Gaussian outcomes.
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SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters
SAGA reduces AI agent task completion time by 1.64x on 64-GPU clusters by scheduling at the full workflow level with execution graphs, affinity batching, and completion-time fairness.
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A Demon that remembers: An agential approach towards quantum thermodynamics of temporal correlations
A classical agent extracts more work from quantum temporal correlations via adaptive strategies bounded by the new Time-Ordered Free Energy, while reinforcement learning achieves polylogarithmic dissipation when learning unknown states.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.