Evaluate and improve the submitted work on graph-based reinforcement learning policies under variable action-space dimensionality, with particular focus on empirically testing whet…
Small language models are increasingly used as lightweight reasoners over structured records, but their behavior under tabular distribution shift is poorly characterized. This pape…
Learning-Reasoning Correspondence (LRC) proposes that neural agents become more sample efficient, robust, and interpretable when explicit inference tuples are generated, serialized…
We test whether a next-nearest-neighbor coupling supplies one finite-size control scale for both the spectral crossover from integrability and the onset of eigenstate thermalizatio…
We study whether sublinear {Nystr\"om} sampling preserves the spectral gap of the symmetric normalized graph Laplacian. For a finite undirected graph with positive degrees, we work…
Randomized SVD is often run with a fixed oversampling parameter, even though approximation quality is driven by the spectrum and by the power-iteration count. We test whether the s…