arXiv:2604.02415v1 Announce Type: cross
Abstract: Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when th…
arXiv:2604.01275v1 Announce Type: cross
Abstract: In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerical solutions to the modular bootstrap equation using machine…
arXiv:2603.05735v2 Announce Type: cross
Abstract: We present an AI agentic measurement of the thrust distribution in $e^{+}e^{-}$ collisions at $\sqrt{s}=91.2$~GeV using archived ALEPH data. The analysis and all note writing is carried out entirely by…
arXiv:2509.19431v2 Announce Type: replace-cross
Abstract: Classifying hadronic jets using their constituents’ kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best perfor…
arXiv:2603.27646v1 Announce Type: new
Abstract: AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. How…
arXiv:2603.26604v1 Announce Type: new
Abstract: Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events…
arXiv:2603.03071v2 Announce Type: replace-cross
Abstract: Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone…