Tootfinder

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs
Hossein Albakri, Kazem Cheshmi
https://arxiv.org/abs/2506.15174 https://

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs
Sparse data structures are commonly used in neural networks to reduce the memory footprint. These data structures are compact but cause irregularities such as random memory accesses, which prevent efficient use of the memory hierarchy. GPUs are a common platform for machine learning practitioners, but running compact data structures on these devices often leads to slow-downs due to inefficient use of computing and memory resources. This paper proposes a new compiler transformation, enumerate-an…

Uncovering Intention through LLM-Driven Code Snippet Description Generation
Yusuf Sulistyo Nugroho, Farah Danisha Salam, Brittany Reid, Raula Gaikovina Kula, Kazumasa Shimari, Kenichi Matsumoto
https://arxiv.org/abs/2506.15453

Uncovering Intention through LLM-Driven Code Snippet Description Generation
Documenting code snippets is essential to pinpoint key areas where both developers and users should pay attention. Examples include usage examples and other Application Programming Interfaces (APIs), which are especially important for third-party libraries. With the rise of Large Language Models (LLMs), the key goal is to investigate the kinds of description developers commonly use and evaluate how well an LLM, in this case Llama, can support description generation. We use NPM Code Snippets, co…

On the Modern Structure of the Gauss-Landau Theorem
Manuel M. Aguilera
https://arxiv.org/abs/2506.15101 https://arxiv.org/pdf/2506.15…

On the Modern Structure of the Gauss-Landau Theorem
We formalize the Gauss-Landau theorem, providing a unified prime factorization approach to computing the GCD and LCM of finite nonzero integer sets. Although commonly used as a heuristic or technique in elementary number theory education, these theorems have not been explicitly formalized or named in the literature. This formalization aims to enhance understanding and facilitate adoption in mathematical instruction and research.

Parallel Data Object Creation: Towards Scalable Metadata Management in High-Performance I/O Library
Youjia Li, Robert Latham, Robert Ross, Ankit Agrawal, Alok Choudhary, Wei-Keng Liao
https://arxiv.org/abs/2506.15114

Parallel Data Object Creation: Towards Scalable Metadata Management in High-Performance I/O Library
High-level I/O libraries, such as HDF5 and PnetCDF, are commonly used by large-scale scientific applications to perform I/O tasks in parallel. These I/O libraries store the metadata such as data types and dimensionality along with the raw data in the same files. While these libraries are well-optimized for concurrent access to the raw data, they are designed neither to handle a large number of data objects efficiently nor to create different data objects independently by multiple processes, as …

A Force Feedback Exoskeleton for Teleoperation Using Magnetorheological Clutches
Zhongyuan Kong, Lei Li, Erwin Ang Tien Yew, Zirui Chen, Wenbo Li, Shiwu Zhang, Jian Yang, Shuaishuai Sun
https://arxiv.org/abs/2506.15124

A Force Feedback Exoskeleton for Teleoperation Using Magnetorheological Clutches
This paper proposes an upper-limb exoskeleton teleoperation system based on magnetorheological (MR) clutches, aiming to improve operational accuracy and enhance the immersive experience during lunar sampling tasks. Conventional exoskeleton teleoperation systems commonly employ active force feedback solutions, such as servo motors, which typically suffer from high system complexity and increased energy consumption. Furthermore, force feedback devices utilizing motors and gear reducers generally …

Fokker-Planck Score Learning: Efficient Free-Energy Estimation under Periodic Boundary Conditions
Daniel Nagel, Tristan Bereau
https://arxiv.org/abs/2506.15653

Fokker-Planck Score Learning: Efficient Free-Energy Estimation under Periodic Boundary Conditions
Accurate free-energy estimation is essential in molecular simulation, yet the periodic boundary conditions (PBC) commonly used in computer simulations have rarely been explicitly exploited. Equilibrium methods such as umbrella sampling, metadynamics, and adaptive biasing force require extensive sampling, while non-equilibrium pulling with Jarzynski's equality suffers from poor convergence due to exponential averaging. Here, we introduce a physics-informed, score-based diffusion framework: by ma…

We all perform tasks in our day-to-day work that are considered 'non-promotable' – these are crucial for project success, but they won't get you promoted. This is commonly known as 'glue work', a term coined by Tanya Reilly. Join Fatima Taj at this year's Berlin Buzzwords as she shares her personal experience of narrowly avoiding the trap of being permanently stuck with glue work, and explains how to handle similar situations.
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Einstein causality of quantum measurements in the Tomonaga-Schwinger picture
Samuel Fedida
https://arxiv.org/abs/2506.14693 https://a…

Einstein causality of quantum measurements in the Tomonaga-Schwinger picture
When quantum measurements are conducted over spacelike-separated regions of spacetime, a natural and commonly assumed physical postulate, called Einstein causality, asserts that they should commute. In this paper, we provide a generalisation to Lüders' rule à la Aharonov-Albert in those globally hyperbolic spacetimes which allow unitarily equivalent Hilbert spaces to be defined along Cauchy hypersurfaces, thus relying on the existence of an interaction picture à la Tomonaga-Schwinger. We sho…

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Yupeng Zhou, Can Cui, Juntong Peng, Zichong Yang, Juanwu Lu, Jitesh H Panchal, Bin Yao, Ziran Wang
https://arxiv.org/abs/2506.14100

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text pairs. However, adapting these models from broad web-scale data to the safety-critical context of driving presents a significant challenge, commonly referred to as domain shift. Existing simulation-based and dataset-driven evaluation methods, although valuable, often fail to capture the full complexity of real-world …

Mobile Application Review Summarization using Chain of Density Prompting
Shristi Shrestha, Anas Mahmoud
https://arxiv.org/abs/2506.14192 https://

Mobile Application Review Summarization using Chain of Density Prompting
Mobile app users commonly rely on app store ratings and reviews to find apps that suit their needs. However, the sheer volume of reviews available on app stores can lead to information overload, thus impeding users' ability to make informed app selection decisions. To address this challenge, we leverage Large Language Models (LLMs) to summarize mobile app reviews. In particular, we use the Chain of Density (CoD) prompt to guide OpenAI GPT-4 to generate abstractive, semantically dense, and easil…