AURORA-AI combines HJB feedback control, Lyapunov monitoring, and a fairness-aware utility into an adaptive policy that reallocates resources across heterogeneous models and outperforms static, round-robin, greedy, LinUCB, and PPO baselines in a stress simulation with bias shocks, drift, and black-s
Getting from generative AI to trustworthy AI: What LLMs might learn from Cyc,
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Proposes Grid-SD2E, a theoretical grid-feedback cognitive learning system combining grid-cell inspiration with Bayesian reasoning for self-reinforcing interaction.
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Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)
AURORA-AI combines HJB feedback control, Lyapunov monitoring, and a fairness-aware utility into an adaptive policy that reallocates resources across heterogeneous models and outperforms static, round-robin, greedy, LinUCB, and PPO baselines in a stress simulation with bias shocks, drift, and black-s
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Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning
Proposes Grid-SD2E, a theoretical grid-feedback cognitive learning system combining grid-cell inspiration with Bayesian reasoning for self-reinforcing interaction.