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.
Leveraging Dynamic Embeddings and Reinforcement Learning with Bayesian Networks for Ransomware Resiliences
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.
citing papers explorer
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Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
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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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.