AIM is a new evaluation framework for explainability in GNNs that combines accuracy, instance-level, and model-level measures, applied to graph kernel networks to create an improved model xGKN.
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2026 2verdicts
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Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
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AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
AIM is a new evaluation framework for explainability in GNNs that combines accuracy, instance-level, and model-level measures, applied to graph kernel networks to create an improved model xGKN.
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Instructions Shape Production of Language, not Processing
Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.