Author

Priyanath Maji

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#artificial intelligence Preprint Sep 2026

Risk-Aware Adaptive Evaluation: Finding High-Impact Failures Under Limited Budgets

Evaluating interactive agents is expensive. Agent behavior is stochastic, so reliability must be measured over repeated trials, but failures are rare and differ widely in how much they matter. Standard benchmarks spend this budget uniformly: a read-only lookup is sampled as often as an irreversible payment action. We i...

Priyanath Maji, S. Chowdhury · 0 citations

#artificial intelligence Preprint Sep 2026

Beyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily

Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar labels. Existing evaluations typically compare architectures under a fixed node-feature representation, leaving unclear whether conclusions about heterophily robustness rema...

Priyanath Maji, Sidharth Gaur, Rajavinoth Paul Durai · 0 citations