Tootfinder

A giant planet transiting a 0.2 solar mass host star
Edward M. Bryant, Andr\'es Jord\'an, Joel D. Hartman, Daniel Bayliss, Elyar Sedaghati, Khalid Barkaoui, Jamila Chouqar, Francisco J. Pozuelos, Daniel P. Thorngren, Mathilde Timmermans, Jose Manuel Almenara, Igor V. Chilingarian, Karen A. Collins, Tianjun Gan, Steve B. Howell, Norio Narita, Enric Palle, Benjamin V. Rackham, Amaury H. M. J. Triaud, Gaspar \'A. Bakos, Rafael Brahm, Melissa J. Hobson, Vincent Van Eylen, Pedro…

A giant planet transiting a 0.2 solar mass host star
Planet formation models suggest that the formation of giant planets is significantly harder around low-mass stars, due to the scaling of protoplanetary disc masses with stellar mass. The discovery of giant planets orbiting such low-mass stars thus imposes strong constraints on giant planet formation processes. Here, we report the discovery of a transiting giant planet orbiting a $0.207 \pm 0.011 M_{\odot}$ star. The planet, TOI-6894 b, has a mass and radius of $M_P = 0.168 \pm 0.022 M_J (53.4 \…

Generalizing Verifiable Instruction Following
Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, Hannaneh Hajishirzi
https://arxiv.org/abs/2507.02833

Generalizing Verifiable Instruction Following
A crucial factor for successful human and AI interaction is the ability of language models or chatbots to follow human instructions precisely. A common feature of instructions are output constraints like ``only answer with yes or no" or ``mention the word `abrakadabra' at least 3 times" that the user adds to craft a more useful answer. Even today's strongest models struggle with fulfilling such constraints. We find that most models strongly overfit on a small set of verifiable constraints from …

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
Dmitry Kondratyev, Benedikt Riedel, Yuan-Tang Chou, Miles Cochran-Branson, Noah Paladino, David Schultz, Mia Liu, Javier Duarte, Philip Harris, Shih-Chieh Hsu
https://arxiv.org/abs/2506.20657

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) approach. SONIC accelerates ML inference by offloading it to local or remote coprocessors to optimize resource utilization. Leveraging its portability to different types of coprocessors, SONIC enhances data processing and model deployment efficiency for cuttin…