Learning--Reasoning Correspondence (LRC) proposes that augmenting training examples with symbolic-style inference tuples can improve small-agent learning. We test this claim in a c…
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…