Author

Kleyton da Costa

PUC-Rio

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#artificial intelligence Preprint Aug 2026

Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse

The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the variational autoencoder rather than argued for. We introduce the generalized graph variational autoencoder (GGVA), which replaces that term with any member of the R\'enyi...

Kleyton da Costa, Bernardo Modenesi, I. Menezes et al. · 0 citations

Preprint Aug 2026

When Should Graph Attention Be Sparse? Learning a Per-Edge Tsallis Index

Graph attention normalizes neighborhood scores with softmax, the maximum-entropy choice under Shannon statistics. But homophilic and heterophilic graphs want different attention shapes, and one fixed normalization cannot serve both. We propose \textbf{LTGA} (\textbf{L}earnable \textbf{T}sallis \textbf{G}raph \textbf{A}...

Kleyton da Costa, Bernardo Modenesi · 0 citations

Preprint Aug 2026

Automata from Agent Traces: Failure and Next-Step Prediction

Behavioral topology is shaped more by the deployment harness than by the LLM, providing a model-agnostic structural primitive for safety auditing and runtime monitoring, and addresses both prediction goals.

Seonglae Cho, F. Fernandez, Umar Mohammed et al. · 2 citations