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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 backbones and enabled tasks.
This survey examines agentic artifact creation, which is defined as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work, and formulate principles for keeping commitments and responsibility explicit, turning feedback into targeted repair, and revalidating affected state after change.
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