arXiv:2509. 17255v2 Announce Type: replace-cross Abstract: We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments on a production synchrotron light source.
By Thorsten Hellert, Drew Bertwistle, Simon C. Leemann, Antonin Sulc, Marco Venturini
arXiv:2606. 07564v1 Announce Type: cross Abstract: This report explores detector design considerations for the Future Circular Collider in its electron-positron mode (FCC-ee) through an extended dialogue between a physicist and an AI assistant.
By Charles Young
arXiv:2607. 09789v1 Announce Type: new Abstract: We introduce PHITSBench, an execution-scored benchmark for the Monte Carlo Particle and Heavy Ion Transport code System (PHITS).
By Xianglin Ji, Svetlana V. Boriskina
arXiv:2606. 13535v1 Announce Type: cross Abstract: Particle physics collider experiments provide Rivet routines as part of the analysis preservation strategy for model-independent measurements.
By Antonio J. Costa, Caterina Doglioni, Christian G\"utschow, Andrew D. Pilkington, Sukanya Sinha
arXiv:2607. 05400v1 Announce Type: cross Abstract: Generative AI models, such as Large Language Models (LLMs) and diffusion models, have demonstrated impressive performance across a wide range of tasks.
By Amitash Nanda, Javier Hernandez Nicolau, Madhusudan Gujral, Mahidhar Tatineni, Amitava Majumdar, Debashis Sahoo
Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring months of specialized effort. Even with high-level synthesis (HLS), designers still need extensive hardware expertise to build high-performance accelerators.