Our development of ToolQA involved a scalable, automated process for dataset curation, along with 13 specialized tools designed for interaction with external knowledge in order to answer questions.

Furthermore, we prove a corresponding non-asymptotic result.

Graph attention networks (GATs) are widely used and often appear robust to noise in node covariates and edges, yet rigorous statistical guarantees demonstrating a provable advantage of GATs over non-attention graph neural networks~(GNNs) are scarce. We partially address this gap for node regression with graph-based errors-in-variables models under simultaneous covariate and edge corruption: responses are generated from latent node-level covariates, but only noise-perturbed versions of the latent covariates are observed; and the sample graph is a random geometric graph created from the node covariates but contaminated by independent Erdős--Rényi edges. We propose and analyze a carefully designed, task-specific GAT that constructs denoised proxy features for regression. We prove that regressing the response variables on the proxies achieves lower error asymptotically in (a) estimating the regression coefficient compared to the ordinary least squares (OLS) estimator on the noisy node covariates, and (b) predicting the response for an unlabelled node compared to a vanilla graph convolutional network~(GCN) -- under mild growth conditions. Our analysis leverages high-dimensional geometric tail bounds and concentration for neighbourhood counts and sample covariances. We verify our theoretical findings through experiments on synthetically generated data. We also perform experiments on real-world graphs and demonstrate the effectiveness of the attention mechanism in several node regression tasks.

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As the use of AI in society grows, addressing emerging biases is essential to prevent systematic discrimination. Several bias detection methods have been proposed, but, with few exceptions, these tend to ignore transparency. Instead, interpretability and explainability are core requirements for algorithmic fairness, even more so than for other algorithmic solutions, given the human-oriented nature of fairness. We present ABIDE (Argumentative BIas detection by DEbate), a novel framework that structures bias detection transparently as debate, guided by an underlying argument graph as understood in (formal and computational) argumentation. The arguments are about the success chances of groups in local neighbourhoods and the significance of these neighbourhoods. We evaluate ABIDE experimentally and demonstrate its strengths in performance against an argumentative baseline.

We thank the reviewers for their feedback. All reviewers found the paper "interesting", and various reviewers commented that "the phenomenon identified here This can be also used to guide architecture selection. Thanks for the suggestion - the findings do hold approximately for any activation function that saturates, i.e. sigmoid, We will comment on this in the camera-ready. Thank you for your suggestion! For Sect. 4.1, the softmax is over just over the

This well-studied problem has plenty of proposed solutions (known as Large Neighborhood Search methods), though.

We are grateful to the reviewers for the insightful comments on our submission. All the minor comments will also be addressed in the revised manuscript. We will update line 153 to " The domain of z can be easily adjusted by translation and dilation after the training process. Reviewer 1: "emphasize the need for gradient evaluations when you state the observation." " .......The first and fourth columns show the relationship between the output and NN is very efficient compared to the evaluating the FEM model in Case (ii).

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