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Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economic dynamics. We test this by placing an instruction-tuned language model inside six Snowdrop-backed dynamic stochastic general equilibrium (DSGE) simulators. At each tur...
Theoretically, it is shown that integrating UTS into GNN optimization strictly extends GNN expressivity beyond the 1-WL hierarchy, and experiments on three graph classification benchmarks show consistent benefits: Graph-UTS, Dual-UTS, and UTS-Pool improve accuracy across all three datasets, Embedding-UTS provides small...
A counterfactual ablation framework called Counterfactual Vision Action Analysis (CVAA) is proposed that systematically removes individual detected objects from front-camera images using photorealistic generative inpainting to prepare counterfactual sets to evaluate the difference in the model's response.
This work proposes ARGTCA, which represents (class, attribute) pairs as nodes in a Symbolic Attribute Graph and trains a Graph Attention Network (GAT) using contrastive objectives to produce structurally informed embeddings that capture inter-attribute dependencies.
G-Loss is presented, a graph-guided loss function that incorporates semi-supervised label propagation to use structural relationships within the embedding manifold to build a document-similarity graph that captures global semantic relationships.
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