Tootfinder

NFL All-Second-Generation Team: Pat Surtain II, Joe Burrow, CMC headline star-studded roster https://www.nytimes.com/athletic/6581612/2025/08/30/nfl-second-generation-team-cmc-joe-burrow/

Evaluating Differentially Private Generation of Domain-Specific Text
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Iqra Zahid, Yuping Wu, Warren Del-Pinto, Goran Nenadic, Siew-Kei Lam, Jie Zhang, Anil A Bharath
https://arxiv.org/abs/2508.20452

Evaluating Differentially Private Generation of Domain-Specific Text
Generative AI offers transformative potential for high-stakes domains such as healthcare and finance, yet privacy and regulatory barriers hinder the use of real-world data. To address this, differentially private synthetic data generation has emerged as a promising alternative. In this work, we introduce a unified benchmark to systematically evaluate the utility and fidelity of text datasets generated under formal Differential Privacy (DP) guarantees. Our benchmark addresses key challenges in d…

NFL All-Second-Generation Team: Pat Surtain II, Joe Burrow, CMC headline star-studded roster https://www.nytimes.com/athletic/6581612/2025/08/30/nfl-second-generation-team-cmc-joe-burrow/

OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning
Yuan Gong, Xionghui Wang, Jie Wu, Shiyin Wang, Yitong Wang, Xinglong Wu
https://arxiv.org/abs/2508.21066

OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning
In this paper, we introduce OneReward, a unified reinforcement learning framework that enhances the model's generative capabilities across multiple tasks under different evaluation criteria using only \textit{One Reward} model. By employing a single vision-language model (VLM) as the generative reward model, which can distinguish the winner and loser for a given task and a given evaluation criterion, it can be effectively applied to multi-task generation models, particularly in contexts with va…

NFL All-Second-Generation Team: Pat Surtain II, Joe Burrow, CMC headline star-studded roster https://www.nytimes.com/athletic/6581612/2025/08/30/nfl-second-generation-team-cmc-joe-burrow/

Worth a read from historian Ed Dunsworth.
"On Generative AI in the Classroom: Give Up, Give In, or Stand Up" (June 2025)
https://activehistory.ca/blog/2025/06/11/on-generative-ai-in-the-classroom-give-up-give-in-or-stand…

On Generative AI in the Classroom: Give Up, Give In, or Stand Up
Edward Dunsworth Two approaches dominate discussion about how professors should handle generative “artificial intelligence” in the classroom: give up or give in. Give up. Faced with a powerful new …

Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge, Charles Moussa, Diego Garlaschelli, Vedran Dunjko, Jordi Tura
https://arxiv.org/abs/2507.22035

Quantum generative modeling for financial time series with temporal correlations
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks. This is especially relevant for financial time series, since we only ever observe one realization of the process, namely the historical evolution of the market, which is further limited by data availability and the age of the market. However, for classical generative adversarial networks it has been shown that genera…

Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement
Zhaorui Yang, Yuxin Qiu, Haichao Zhu, Qian Zhang
https://arxiv.org/abs/2507.21271 https://…

Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement
[Context] Modern AI applications increasingly process highly structured data, such as 3D meshes and point clouds, where test input generation must preserve both structural and semantic validity. However, existing fuzzing tools and input generators are typically handcrafted for specific input types and often generate invalid inputs that are subsequently discarded, leading to inefficiency and poor generalizability. [Objective] This study investigates whether test inputs for structured domains can…

Defining ethically sourced code generation
Zhuolin Xu, Chenglin Li, Qiushi Li, Shin Hwei Tan
https://arxiv.org/abs/2507.19743 https://arxiv.org/pdf/2507.19…

Defining ethically sourced code generation
Several code generation models have been proposed to help reduce time and effort in solving software-related tasks. To ensure responsible AI, there are growing interests over various ethical issues (e.g., unclear licensing, privacy, fairness, and environment impact). These studies have the overarching goal of ensuring ethically sourced generation, which has gained growing attentions in speech synthesis and image generation. In this paper, we introduce the novel notion of Ethically Sourced Code …