arXiv:2603.10074v1 Announce Type: new Abstract: We construct a minimal task that isolates conditional learning in neural networks: a surjective map with K-fold ambiguity, resolved by a selector token z, so H(A | B) = log K while H(A | B,…
arXiv:2305.12292v4 Announce Type: replace Abstract: Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible. Unfortunately, existing methods for matrix completion are heuristics that, while highly scalable and…
arXiv:2603.10078v1 Announce Type: new Abstract: Stochastic port-Hamiltonian systems represent open dynamical systems with dissipation, inputs, and stochastic forcing in an energy based form. We introduce stochastic port-Hamiltonian neural networks, SPH-NNs, which parameterize the Hamiltonian with a feedforward network and enforce…
arXiv:2603.10079v1 Announce Type: new Abstract: We analyse SGD training of a shallow, fully connected network in the NTK scaling and provide a quantitative theory of the catapult phase. We identify an explicit criterion separating two behaviours: When an explicit function…
MIT astronomers are developing a new way to detect, monitor, and mitigate the threats posed by smaller asteroids to our critical space infrastructure.
arXiv:2410.08727v2 Announce Type: replace-cross Abstract: Diffusion models power leading generative AI, but when and how they memorize training data, especially on low-dimensional manifolds, remains unclear. We find memorization emerges gradually, not abruptly: as data become scarce, diffusion models experience a…
arXiv:2510.23914v2 Announce Type: replace Abstract: While Value Iteration (VI) is one of the most fundamental algorithms in Reinforcement Learning, its theoretical convergence guarantees still exhibit a persistent mismatch with empirical behavior. In the discounted-reward case, classical theory guarantees geometric convergence…
arXiv:2602.09375v3 Announce Type: replace Abstract: We propose LaPha, a method for training AlphaZero-like LLM agents in a Poincar’e latent space. Under LaPha, the search process can be visualized as a tree rooted at the prompt and growing outward from the…
arXiv:2603.10886v1 Announce Type: cross Abstract: We propose novel kernel-based tests for assessing the equivalence between distributions. Traditional goodness-of-fit testing is inappropriate for concluding the absence of distributional differences, because failure to reject the null hypothesis may simply be a result…
arXiv:2504.04371v3 Announce Type: replace Abstract: Traditional Support Vector Machine (SVM) classification is carried out by finding the max-margin classifier for the training data that divides the margin space into two equal sub-spaces. This study demonstrates limitations of performing Support Vector…
