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The analysis indicates that inference can rival or exceed training energy over a deployment lifetime, and that agentic workflows can multiply the energy of equivalent single-pass inference by one to several orders of magnitude depending on the number of model and tool calls.
This work presents the first closed-form theoretical characterization of GNN calibration under distribution shift, and shows that calibration is governed by a single scalar quantity that explicitly depends on structural changes between the source and target graphs, as well as feature quality.
This survey organises the field around a single design space, derives the spectral and spatial formulations from shared first principles, and connects expressive power to the Weisfeiler-Leman hierarchy with explicit statements of what current architectures can and cannot separate.
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