arXiv:2512.18147v2 Announce Type: replace-cross
Abstract: Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficie…
arXiv:2604.00919v1 Announce Type: cross
Abstract: Variational autoencoders (VAEs) learn compact latent representations of complex data, but their generative capacity is fundamentally constrained by the choice of prior distribution over the latent spac…
arXiv:2604.01197v1 Announce Type: cross
Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study the problem of learning to generate mixed states on a fini…
arXiv:2603.29072v1 Announce Type: cross
Abstract: Point-cloud persistent homology (PH) — computing alpha or Rips complexes on spin-position point clouds — has been widely applied to detect phase transitions in classical spin models since Donato et a…
arXiv:2603.29932v1 Announce Type: cross
Abstract: We explore the capability of a Large Language Model (LLM) to perform specific computations in mathematical physics: the task is to compute the coordinate Bethe Ansatz solution of selected integrable sp…
arXiv:2602.18482v2 Announce Type: replace-cross
Abstract: Sampling equilibrium distributions is fundamental to statistical mechanics. While flow matching has emerged as scalable state-of-the-art paradigm for generative modeling, its potential for equi…
arXiv:2603.23626v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as optimization modules in agentic systems, yet the fundamental limits of such LLM-mediated improvement remain poorly understood. Here we propose a …