Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi, James Zou • Published: 2026-02-12
Despite speech recognition systems achieving low word error rates on standard benchmarks, they often fail on short, high-stakes utterances in real-world deployments. Here, we study this failure mode in a high-stakes task: the transcription of U.S. street names as spoken by U.S. participants. We evaluate 15 models from OpenAI, Deepgram, Google, and Microsoft on recordings from linguistically divers...
Si Yan Koh, Weifan Wu, Kelvin Onggadinata, Arghya Maity, Mark Chiyuan Ma, Calvin Pei Yu Wong, Kuan Eng Johnson Goh, Bent Weber, Hui Khoon Ng, Teck Seng Koh • Published: 2026-02-12
Superconducting resonators coupled to solid-state qubits offer a scalable architecture for long-range entangling operations and fast, high-fidelity readout. Realizing this requires low photon-loss rates and qubits with tunable electric dipole moments that couple strongly to the resonator's electric field while maintaining long coherence times. For spin qubits, spin-photon coupling is typically ach...
Tony Feng, Trieu H. Trinh, Garrett Bingham, Dawsen Hwang, Yuri Chervonyi, Junehyuk Jung, Joonkyung Lee, Carlo Pagano, Sang-hyun Kim, Federico Pasqualotto, Sergei Gukov, Jonathan N. Lee, Junsu Kim, Kaiying Hou, Golnaz Ghiasi, Yi Tay, YaGuang Li, Chenkai Kuang, Yuan Liu, Hanzhao Lin, Evan Zheran Liu, Nigamaa Nayakanti, Xiaomeng Yang, Heng-Tze Cheng, Demis Hassabis, Koray Kavukcuoglu, Quoc V. Le, Thang Luong • Published: 2026-02-10
Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requires navigating vast literature and constructing long-horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively gene...
Barkay Guttel, Danielle Gov, Noam Netzer, Uri Goldblatt, Sergey Hazanov, Lalit M. Joshi, Alessandro Romito, Yuval Gefen, Parveen Kumar, Kyrylo Snizhko, Fabien Lafont, Serge Rosenblum • Published: 2026-02-02
Quantum mechanics predicts that unobserved systems may exist in a superposition of states, yet measurement produces definite outcomes, a tension at the heart of the quantum-to-classical boundary. How the transformation between these opposing regimes unfolds as observation strength increases has remained experimentally unexplored. Here, by continuously tuning the measurement strength on a supercond...
Peter Brearley, Philipp Pfeffer • Published: 2025-11-24
Dissipation and irreversibility are central to most physical processes, yet they lead to non-unitary dynamics that are challenging to realise on quantum processors. High-order operator splitting, also known as high-order product formulas, is an attractive approach for simulating unitary dynamics, yet conventional high-order splitting schemes introduce negative time steps that are numerically unsta...
Jacopo De Santis, Balázs Dura-Kovács, Mehmet Öncü, Adrien Bouscal, Dimitrios Vasileiadis, Johannes Zeiher • Published: 2026-02-12
Scalable quantum computers and quantum networks require the combination of quantum processing nodes with efficient light-matter interfaces to distribute quantum information in local or long-distance quantum networks. Neutral-atom arrays have both been coupled to Rydberg states to enable high-fidelity quantum gates in universal processing architectures, and to optical cavities to realize interfaces...
Jittarin Jetwiriyanon, Teo Susnjak, Surangika Ranathunga • Published: 2026-02-12
Many universities face increasing financial pressure and rely on accurate forecasts of commencing enrolments. However, enrolment forecasting in higher education is often data-sparse; annual series are short and affected by reporting changes and regime shifts. Popular classical approaches can be unreliable, as parameter estimation and model selection are unstable with short samples, and structural ...
Yuzhe Shang, Pengzhi Gao, Wei Liu, Jian Luan, Jinsong Su • Published: 2026-02-12
Open large language models (LLMs) have demonstrated improving multilingual capabilities in recent years. In this paper, we present a study of open LLMs for multilingual machine translation (MT) across a range of languages, and investigate the effects of model scaling and data scaling when adapting open LLMs to multilingual MT through continual pretraining and instruction finetuning. Based on the G...