The paper maps LLM agent architectures onto a six-level continuum and argues that higher levels can enable simulation of emergent social phenomena while requiring attention to reproducibility and ethical issues.
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2 Pith papers cite this work. Polarity classification is still indexing.
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TL-RL-FusionNet uses frozen transfer learning backbones and a Q-learning agent to adaptively reweight training samples for ransomware detection, reporting 99.1% accuracy on a 1000-sample dataset.
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TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats
TL-RL-FusionNet uses frozen transfer learning backbones and a Q-learning agent to adaptively reweight training samples for ransomware detection, reporting 99.1% accuracy on a 1000-sample dataset.