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This work studies the LLC of a graph attention model by exploiting symmetries in both the graph structure and the attention parameters, and develops an analytic framework through a teacher--student setting and explicit LLC estimates after considering the symmetry--induced degeneracies.
This work studies how activation-based detection changes when agents are explicitly informed that their internal activations are being monitored, and when they additionally receive feedback from the monitor.
These results resolve the corresponding questions regarding the parameterized complexity status for zonotope norm maximization and two-layer ICNN Lipschitz constants.
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