Tootfinder

Mormon women are finally allowed to wear sleeveless tops. Here's why some are grieving.
https://religionnews.com/2025/06/27/mormon-women-are-finally-allowed-to-wear-sleeveless-tops-heres-why-some-are-grieving/

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation
Xinyi Ni, Haonan Jian, Qiuyang Wang, Vedanshi Chetan Shah, Pengyu Hong
https://arxiv.org/abs/2506.19998 …

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation
REST APIs play important roles in enriching the action space of web agents, yet most API-based agents rely on curated and uniform toolsets that do not reflect the complexity of real-world APIs. Building tool-using agents for arbitrary domains remains a major challenge, as it requires reading unstructured API documentation, testing APIs and inferring correct parameters. We propose Doc2Agent, a scalable pipeline to build agents that can call Python-based tools generated from API documentation. Do…

Scalable quantum interference in integrated lithium niobate nanophotonics
Tristan Kuttner, Alessandra Sabatti, Jost Kellner, Rachel Grange, Robert J. Chapman
https://arxiv.org/abs/2506.20519

Scalable quantum interference in integrated lithium niobate nanophotonics
Photonics has emerged as one of the leading platforms for the implementation of real-world-applicable quantum technologies, enabling secure communication, enhanced sensing capabilities, as well as resolving previously intractable computational challenges. However, to harness the full potential of the photonics platform, several engineering feats need to be accomplished, among those is the quest for a scalable source of pure single photons. While single photon sources can be implemented in a var…

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
Maxim Moraru, Weiyi Xia, Zhuo Ye, Feng Zhang, Yongxin Yao, Ying Wai Li, Cai-Zhuang Wang
https://arxiv.org/abs/2506.21449

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from executio…

Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
Qiong Li, Chao Fang, Longwei Huang, Jun Lin, Zhongfeng Wang
https://arxiv.org/abs/2505.19096

Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
While posit format offers superior dynamic range and accuracy for transprecision computing, its adoption in RISC-V processors is hindered by the lack of a unified solution for lightweight, precision-scalable, and IEEE-754 arithmetic compatible hardware implementation. To address these challenges, we enhance RISC-V processors by 1) integrating dedicated posit codecs into the original FPU for lightweight implementation, 2) incorporating multi/mixed-precision support with dynamic exponent size for…

ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing
Long Xing, Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jinsong Li, Shuangrui Ding, Weiming Zhang, Nenghai Yu, Jiaqi Wang, Feng Wu, Dahua Lin
https://arxiv.org/abs/2506.19848

ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing
This paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality image captioning lie in the inherent biases of LVLMs: multimodal bias resulting in imbalanced descriptive granularity, offering detailed accounts of some elements while merely skimming over others; linguistic bias leading to hallucinated descriptions of non-existent objects. To address these issues, we propose a scalable …

Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability
Faiek Ahsan, Saptarshi Maiti, Zihao Chen, Jakob Kaiser, Ankita Nandi, Madhuvanthi Srivatsav, Johannes Schemmel, Andreas G. Andreou, Jason Eshraghian, Chetan Singh Thakur, Shantanu Chakrabartty
https://arxiv.org…

Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability
We report a higher-order neuromorphic Ising machine that exhibits superior scalability compared to architectures based on quadratization, while also achieving state-of-the-art quality and reliability in solutions with competitive time-to-solution metrics. At the core of the proposed machine is an asynchronous autoencoder architecture that captures higher-order interactions by directly manipulating Ising clauses instead of Ising spins, thereby maintaining resource complexity independent of inter…

While the world has been watching the Middle East, #Ukraine has been under intense bombardement.
There, but for the grace of geography...
Kyiv’s sleepless nights under fire - https://on.ft.com/4lcdgig via @FT

Scalable Subset Selection in Linear Mixed Models
Ryan Thompson, Matt P. Wand, Joanna J. J. Wang
https://arxiv.org/abs/2506.20425 https://

Scalable Subset Selection in Linear Mixed Models
Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine or adaptive marketing. Nowadays, this type of data is increasingly wide, sometimes containing thousands of candidate predictors, necessitating sparsity for prediction and interpretation. However, existing sparse learning methods for LMMs do not scale well beyond tens or hundreds of predictors, leaving a large gap compared with sparse methods fo…

Scalable GPU Performance Variability Analysis framework
Ankur Lahiry, Ayush Pokharel, Seth Ockerman, Amal Gueroudji, Line Pouchard, Tanzima Z. Islam
https://arxiv.org/abs/2506.20674

Scalable GPU Performance Variability Analysis framework
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insight and hinder the integration of performance analytics into automated workflows. Existing analysis tools typically process data sequentially, making them ill-suited for HPC workflows with growing trace complexity and volume. We introduce a distributed data analysis framework that scales with dataset size and compute av…