Tootfinder

A profile of Anton Pavlovsky, CEO of Kyiv-based Headway Inc, which develops gamified educational apps and has 160M users across its five-product portfolio (Martina Di Licosa/Forbes)
http://www.forbes.com/sites/martinadilic…

This Ukrainian Entrepreneur Built His EdTech App Into A Global Phenomenon—In The Middle Of A War
Anton Pavlovsky founded Headway Inc to help busy people improve their lives by reading more books—and avoiding doomscrolling. Air raids be damned.

NBCUniversal changed MSNBC's name "to accelerate the distinction between MSNBC and NBC News"; MSNBC has been undergoing aggressive hiring for ~100 new positions (Alex Sherman/CNBC)
https://www.cnbc.com/2025/08/18/comcast-versant-rename-msnbc-peac…

Versant to rename MSNBC, drop famed peacock logos in Comcast separation
They're the first real public-facing changes in Versant's upcoming separation from Comcast's NBCUniversal.

DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning
Weize Liu, Yongchi Zhao, Yijia Luo, Mingyu Xu, Jiaheng Liu, Yanan Li, Xiguo Hu, Yuchi Xu, Wenbo Su, Bo Zheng
https://arxiv.org/abs/2508.12726

DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning
Large language models (LLMs) have achieved remarkable success in many natural language tasks but still struggle with complex, multi-step reasoning, particularly across diverse disciplines. Existing reasoning datasets often either lack disciplinary breadth or the structural depth necessary to elicit robust reasoning behaviors. We propose DESIGNER: a DESIGN-logic-guidEd Reasoning data synthesis pipeline that leverages naturally available, extensive raw documents (book corpus and web corpus) to ge…

Surya: Foundation Model for Heliophysics
Sujit Roy, Johannes Schmude, Rohit Lal, Vishal Gaur, Marcus Freitag, Julian Kuehnert, Theodore van Kessel, Dinesha V. Hegde, Andr\'es Mu\~noz-Jaramillo, Johannes Jakubik, Etienne Vos, Kshitiz Mandal, Ata Akbari Asanjan, Joao Lucas de Sousa Almeida, Amy Lin, Talwinder Singh, Kang Yang, Chetraj Pandey, Jinsu Hong, Berkay Aydin, Thorsten Kurth, Ryan McGranaghan, Spiridon Kasapis, Vishal Upendran, Shah Bahauddin, Daniel da Silva, Nikolai V. Pogo…

Surya: Foundation Model for Heliophysics
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, includin…

Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing
Sizhen Bian, Mengxi Liu, Paul Lukowicz
https://arxiv.org/abs/2507.13520

Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing
Passive body-area electrostatic field sensing, also referred to as human body capacitance (HBC), is an energy-efficient and non-intrusive sensing modality that exploits the human body's inherent electrostatic properties to perceive human behaviors. This paper presents a focused overview of passive HBC sensing, including its underlying principles, historical evolution, hardware architectures, and applications across research domains. Key challenges, such as susceptibility to environmental variat…

Composable logical gate error in approximate quantum error correction: reexamining gate implementations in Gottesman-Kitaev-Preskill codes
Lukas Brenner, Beatriz Dias, Robert Koenig
https://arxiv.org/abs/2509.14658

Composable logical gate error in approximate quantum error correction: reexamining gate implementations in Gottesman-Kitaev-Preskill codes
Quantifying the accuracy of logical gates is paramount in approximate error correction, where perfect implementations are often unachievable with the available set of physical operations. To this end, we introduce a single scalar quantity we call the (composable) logical gate error. It captures both the deviation of the logical action from the desired target gate as well as leakage out of the code space. It is subadditive under successive application of gates, providing a simple means for analy…

Evaluation Awareness Scales Predictably in Open-Weights Large Language Models
Maheep Chaudhary, Ian Su, Nikhil Hooda, Nishith Shankar, Julia Tan, Kevin Zhu, Ashwinee Panda, Ryan Lagasse, Vasu Sharma
https://arxiv.org/abs/2509.13333

Evaluation Awareness Scales Predictably in Open-Weights Large Language Models
Large language models (LLMs) can internally distinguish between evaluation and deployment contexts, a behaviour known as \emph{evaluation awareness}. This undermines AI safety evaluations, as models may conceal dangerous capabilities during testing. Prior work demonstrated this in a single $70$B model, but the scaling relationship across model sizes remains unknown. We investigate evaluation awareness across $15$ models scaling from $0.27$B to $70$B parameters from four families using linear pr…

Activity in White Dwarf Debris Disks I: Spitzer Legacy Reveals Variability Incompatible with the Canonical Model
Hiba Tu Noor, Jay Farihi, Scott J. Kenyon, Roman R. Rafikov, Mark C. Wyatt, Kate Y. L. Su, Carl Melis, Andrew Swan, Thomas G. Wilson, Boris T. G\"ansicke, Amy Bonsor, Laura K. Rogers, Seth Redfield, Mukremin Kilic
https://a…

Activity in White Dwarf Debris Disks I: Spitzer Legacy Reveals Variability Incompatible with the Canonical Model
This study presents all available, multi-epoch 3.6 and 4.5 $μ$m photometry from Spitzer Space Telescope observations of white dwarf debris disks, including weekly cadence observations of 16 relatively bright systems, and 5 h staring-mode observations for five of these. Significant variability is detected in 85 per cent of disks and across all timescales probed, from minutes to weeks to years, where the largest flux changes correlate with the longest time baselines, and the infrared excesses pe…

Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference
Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe, Hasan Kurban
https://arxiv.org/abs/2508.14735

Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference
Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplored. We present a controlled evaluation framework for multilingual natural language inference (NLI) that generates synthetic, logic-based premise-hypothesis pairs and translates them into a typologically diverse set of languages. This design enables precise control over semantic relations and allows testing in both mon…

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Hao Zhang, Zhen Kan, Weiwei Shang, Yongduan Song
https://arxiv.org/abs/2509.14939

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Despite recent advances in dexterous manipulations, the manipulation of articulated objects and generalization across different categories remain significant challenges. To address these issues, we introduce DART, a novel framework that enhances a diffusion-based policy with affordance learning and linear temporal logic (LTL) representations to improve the learning efficiency and generalizability of articulated dexterous manipulation. Specifically, DART leverages LTL to understand task semantic…