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Trunk Tools, which provides AI services to organize construction documentation, raised a $40M Series B led by Insight, bringing its total funding to $70M (Kyt Dotson/SiliconANGLE)
https://siliconangle.com/2025/07/24/trunk-too…

Trunk Tools raises $40M to revolutionize how construction teams interact with project data - SiliconANGLE
Trunk Tools raises $40M to revolutionize how construction teams interact with project data - SiliconANGLE

A particle view of many-body electronic structure with neural network wavefunction
Zichen Wang, Weizhong Fu, Zhe Li, Weiluo Ren, Ji Chen
https://arxiv.org/abs/2508.13751 https:/…

A particle view of many-body electronic structure with neural network wavefunction
In the study of electronic structure, the wavefunction view dominates the current research landscape and forms the theoretical foundation of modern quantum mechanics. In contrast, Valence Bond (VB) theory represents chemical bonds as shared electron-pairs and can provide an intuitive, particle-based insight into chemical bonding. In this work, using a newly developed Periodic Dynamic Voronoi Metropolis Sampling (PDVMS) method, we project classical many-body electronic configurations from the ne…

KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang, Sivaprasad Sudhir, Om Chabra, Anna Zeng, Anton A. Zabreyko, Chenning Li, Ferdi Kossmann, Jialin Ding, Jun Chen, Markos Markakis, Matthew Russo, Weiyang Wang, Ziniu Wu, Michael J. Cafarella, Lei Cao, Samuel Madden, Tim Kraska

KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Constructing real-world data-to-insight pipelines often involves data extraction from data lakes, data integration across heterogeneous data sources, and diverse operations from data cleaning to analysis. The design and implementation of data science pipelines require domain knowledge, technical expertise, and even project-specific insights. AI systems have shown remarkable reasoning, coding, and understanding capabilities. However, it remains unclear to what extent these capabilities translate…

OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World
Katherine Liu, Sergey Zakharov, Dian Chen, Takuya Ikeda, Greg Shakhnarovich, Adrien Gaidon, Rares Ambrus
https://arxiv.org/abs/2508.03669

OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World
We would like to estimate the pose and full shape of an object from a single observation, without assuming known 3D model or category. In this work, we propose OmniShape, the first method of its kind to enable probabilistic pose and shape estimation. OmniShape is based on the key insight that shape completion can be decoupled into two multi-modal distributions: one capturing how measurements project into a normalized object reference frame defined by the dataset and the other modelling a prior …

This https://arxiv.org/abs/2504.07409 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMS_…

RLibm-MultiRound: Correctly Rounded Math Libraries Without Worrying about the Application's Rounding Mode
Our RLibm project generates a single implementation for an elementary function that produces correctly rounded results for multiple rounding modes and representations with up to 32-bits. They are appealing for developing fast reference libraries without double rounding issues. The key insight is to build polynomials that produce the correctly rounded result for a representation with two additional bits when compared to the largest target representation and with the "non-standard" round-to-odd r…