arXiv:2603.00181v1 Announce Type: new Abstract: A recent report on “Learning the natural history of human disease with generative transformers” created an opportunity to assess the engineering challenge of delivering user-facing Generative AI applications in privacy-sensitive domains. The application of these…
arXiv:2506.06558v3 Announce Type: replace Abstract: Learning dynamical systems that respect physical symmetries and constraints remains a fundamental challenge in data-driven modeling. Integrating physical laws with graph neural networks facilitates principled modeling of complex N-body dynamics and yields accurate and permutation-invariant…
arXiv:2509.02391v3 Announce Type: replace Abstract: The success of federated learning (FL) ultimately depends on how strategic participants behave under partial observability, yet most formulations still treat FL as a static optimization problem. We instead view FL deployments as governed strategic…
arXiv:2602.21468v2 Announce Type: replace-cross Abstract: The spin-$1/2$ $J_1$-$J_2$ Heisenberg model on the square lattice exhibits a debated intermediate phase between N’eel antiferromagnetic and stripe ordered regimes, with competing theories proposing plaquette valence bond, nematic, and quantum spin liquid ground states.…
arXiv:2503.01441v2 Announce Type: replace-cross Abstract: We consider the problem of minimizing a smooth and convex function over the $n$-dimensional spectrahedron — the set of real symmetric $ntimes n$ positive semidefinite matrices with unit trace, which underlies numerous applications in statistics,…
arXiv:2603.00044v1 Announce Type: new Abstract: Advancing trustworthy AI requires principled software engineering approaches to model evaluation. Graph Neural Networks (GNNs) have achieved remarkable success in processing graph-structured data, however, their expressiveness in capturing fundamental graph properties remains an open challenge.…
arXiv:2601.18753v2 Announce Type: replace Abstract: The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: data-driven hallucinations and reasoning-driven hallucinations. However,…
arXiv:2603.00042v1 Announce Type: new Abstract: We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baselines for heavy-tailed spectra. However, prior attempts fail to realize this potential, trailing state-of-the-art 1-bit methods. We attribute…
arXiv:2603.00043v1 Announce Type: new Abstract: This paper presents a novel approach to reinforcement learning (RL) for control systems that provides probabilistic stability guarantees using finite data. Leveraging Lyapunov’s method, we propose a probabilistic stability theorem that ensures mean square stability…
arXiv:2603.00041v1 Announce Type: new Abstract: Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no explicit time order, whereas recovering causal structures from time series data remains…
