AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces MUGEN, to its knowledge the first framework for generating unlearnable graph examples that jointly protect all enabled tasks, and devise a Task-Aligned Separability Objective (TASO), which leverages task prediction and classwise separability to strengthen unlearnability and its transfer across GNN...
Examination of the impact of agent diversity, agent open-mindedness, and human–AI collaboration (HAIC) in a multi-LLM-agent system for automated content analysis demonstrates reliable and accurate measurement of four communication variables across three datasets, with improved performance following agent discussion.
Comprehensive experiments demonstrate that TextCloak consistently impairs unauthorized fine-tuning while maintaining text utility for legitimate use, highlighting its broad applicability as a practical defense against unauthorized LLM exploitation.
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