The paper presents an autonomous research laboratory where large‑language‑model agents collaborate to formulate, evaluate, and experimentally test research questions. In a study of quantum spins in diamond, the agents determined the minimal calibration settings for predicting microwave pulse responses, performed computational checks, and designed experiments to discriminate between competing predictions. When initial data drifted, the agents deemed the result inconclusive, revised the protocol, and ultimately confirmed the theoretical predictions with a successful experiment.
By El Mustapha Mansouri, Eikichi Kimura, Masato Koga, Keigo Arai
arXiv:2607. 25145v1 Announce Type: cross Abstract: We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond.
By Takuya Isogawa, Ryotaro Okabe, Nutdech Phadetsuwannukun, Mingda Li, Paola Cappellaro
arXiv:2603. 13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher.
By Haonan Huang
arXiv:2607. 25834v1 Announce Type: cross Abstract: Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities.
By Constantin Dalyac, Alexandre Dauphin, Lo\"ic Henriet, Christophe Jurczak
The paper introduces QUANTA, an agentic AI framework designed for autonomous characterization of quantum materials using a Physical Property Measurement System. QUANTA integrates literature retrieval, experimental planning, troubleshooting, and data interpretation, with a deterministic control layer ensuring safe instrument operation. Demonstrations include transport measurements on the Weyl semimetal TaP and nonlinear response studies in the magnetic material Co2MnGa, showcasing QUANTA’s generalizable, evidence-driven, closed-loop capabilities.
By Eunbi Rha, Abhijatmedhi Chotrattanapituk, Mouyang Cheng, Chung-tao Chou, Shenyong Ye, Ashley S. Dale, Long Ju, Luqiao Liu, Mingda Li
arXiv:2602. 17902v2 Announce Type: replace Abstract: Large language models (LLMs) can plan scientific workflows and generate code, but these capabilities do not specify how scientific state is validated, transferred and recorded across heterogeneous computational and experimental operations.
By Jiaru Bai, Abdulrahman Aldossary, Thomas Swanick, Marcel M\"uller, Yeonghun Kang, Changhyeok Choi, Naruki Yoshikawa, Zijian Zhang, Jin Won Lee, Tsz Wai Ko, Aiwei Yin, Mohammad Ghazi Vakili, Chris Crebolder, Varinia Bernales, Al\'an Aspuru-Guzik