Tootfinder

Purcell enhancement of photogalvanic currents in a van der Waals plasmonic self-cavity
Xinyu Li, Jesse Hagelstein, Gunda Kipp, Felix Sturm, Kateryna Kusyak, Yunfei Huang, Benedikt F. Schulte, Alexander M. Potts, Jonathan Stensberg, Victoria Quir\'os-Cordero, Chiara Trovatello, Zhi Hao Peng, Chaowei Hu, Jonathan M. DeStefano, Michael Fechner, Takashi Taniguchi, Kenji Watanabe, P. James Schuck, Xiaodong Xu, Jiun-Haw Chu, Xiaoyang Zhu, Angel Rubio, Marios H. Michael, Matthew W. Day, H…

Purcell enhancement of photogalvanic currents in a van der Waals plasmonic self-cavity
Cavities provide a means to manipulate the optical and electronic responses of quantum materials by selectively enhancing light-matter interaction at specific frequencies and momenta. While cavities typically involve external structures, exfoliated flakes of van der Waals (vdW) materials can form intrinsic self-cavities due to their small finite dimensions, confining electromagnetic fields into plasmonic cavity modes, characterized by standing-wave current distributions. While cavity-enhanced p…

Live Music Models
Lyria Team, Antoine Caillon, Brian McWilliams, Cassie Tarakajian, Ian Simon, Ilaria Manco, Jesse Engel, Noah Constant, Pen Li, Timo I. Denk, Alberto Lalama, Andrea Agostinelli, Anna Huang, Ethan Manilow, George Brower, Hakan Erdogan, Heidi Lei, Itai Rolnick, Ivan Grishchenko, Manu Orsini, Matej Kastelic, Mauricio Zuluaga, Mauro Verzetti, Michael Dooley, Ondrej Skopek, Rafael Ferrer, Zal\'an Borsos, \"Aaron van den Oord, Douglas Eck, Eli Collins, Jason Baldrid…

Live Music Models
We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release Magenta RealTime, an open-weights live music model that can be steered using text or audio prompts to control acoustic style. On automatic metrics of music quality, Magenta RealTime outperforms other open-weights music generation models, despite using fewer parameters and offering first-of-its-kind live generation capa…

Crosslisted article(s) found for cs.CC. https://arxiv.org/list/cs.CC/new
[1/1]:
- Mistake-bounded online learning with operation caps
Jesse Geneson, Meien Li, Linus Tang

Linking thermodynamic correlation signatures and superconductivity in twisted trilayer graphene
Jesse C. Hoke, Yifan Li, Yuwen Hu, Julian May-Mann, Kenji Watanabe, Takashi Taniguchi, Trithep Devakul, Aaron Sharpe, Benjamin E. Feldman
https://arxiv.org/abs/2509.07977

Linking thermodynamic correlation signatures and superconductivity in twisted trilayer graphene
Twisted graphene multilayers exhibit strong electronic correlations, which manifest in a range of experimental signatures. Yet how these signatures relate to each other and the microscopic ground states-and how twist angle and band structure reshape them-remains poorly understood. Here we study this interplay by correlating local thermodynamic and transport measurements in a twisted trilayer graphene (TTG) sample with unequal angles and flat electronic bands. We use a scanning single-electron t…

Crosslisted article(s) found for cs.DM. https://arxiv.org/list/cs.DM/new
[1/1]:
- Mistake-bounded online learning with operation caps
Jesse Geneson, Meien Li, Linus Tang

Language Models Improve When Pretraining Data Matches Target Tasks
David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice, Suzie Petryk, Yuri Gorokhov, Jeffrey Li, Alex Fang, Josh Gardner, Tom Gunter, Afshin Dehghan
https://arxiv.org/abs/2507.12466

Language Models Improve When Pretraining Data Matches Target Tasks
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies, train models, measure benchmark performance, then refine accordingly. This raises a natural question: what happens when we make this optimization explicit? To explore this, we propose benchmark-targeted ranking (BETR), a simple method that selects pretraining documents based on similarity to benchmark training examp…

Leakage-Resilient Extractors against Number-on-Forehead Protocols
Eshan Chattopadhyay, Jesse Goodman
https://arxiv.org/abs/2506.12595 https://

Leakage-Resilient Extractors against Number-on-Forehead Protocols
Given a sequence of $N$ independent sources $\mathbf{X}_1,\mathbf{X}_2,\dots,\mathbf{X}_N\sim\{0,1\}^n$, how many of them must be good (i.e., contain some min-entropy) in order to extract a uniformly random string? This question was first raised by Chattopadhyay, Goodman, Goyal and Li (STOC '20), motivated by applications in cryptography, distributed computing, and the unreliable nature of real-world sources of randomness. In their paper, they showed how to construct explicit low-error extracto…