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The Laude Institute, a nonprofit that runs the K Prize multi-round AI coding challenge, says a Brazilian prompt engineer won with just 7.5% of the right answers (Russell Brandom/TechCrunch)
https://techcrunch.com/2025/07/23/a-ne

A new AI coding challenge just published its first results – and they aren’t pretty | TechCrunch
A new AI coding challenge has revealed its first winner — and set a new bar for AI-powered software engineers.  On Wednesday at 5pm PST, the

A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications
Tolga \c{C}ukur, Salman U. H. Dar, Valiyeh Ansarian Nezhad, Yohan Jun, Tae Hyung Kim, Shohei Fujita, Berkin Bilgic
https://arxiv.org/abs/2507.16715

A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications
MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple structural sequences that provide complementary information for differential diagnosis, while research protocols often incorporate advanced functional, diffusion, spectroscopic, and relaxometry sequences to capture multidimensional insights into tissue structure and composition. However, these capabilities come at the…

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Aleksandr Perevalov, Andreas Both
https://arxiv.org/abs/2507.16971

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Accessing knowledge via multilingual natural-language interfaces is one of the emerging challenges in the field of information retrieval and related ones. Structured knowledge stored in knowledge graphs can be queried via a specific query language (e.g., SPARQL). Therefore, one needs to transform natural-language input into a query to fulfill an information need. Prior approaches mostly focused on combining components (e.g., rule-based or neural-based) that solve downstream tasks and come up wi…

"How to get the most out of tech while freeing ourselves from the lunatics in Silicon Valley"
Says Ian Dunt in his article on a corporate Substack page linked from his block-chain owned Bluesky account.
He does so without using the words "Fedi" "blog", "decentralize" "open source", "free software", or "mastodon", and while calling Facebook and Instagram "seemingly more benign".
There must be a form of moderate tech disinvestment, which allows us the things we benefit from while removing the things which harm us.
He pleads, admitting
I have no answers for this
Yeah, no kidding.
But he has stopped using Amazon so much and buys more DVDs now, and tries to use a tablet more than a phone!?
Well done I guess.

Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
Sajratul Y. Rubaiat, Hasan M. Jamil
https://arxiv.org/abs/2506.17580

Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
The exponential growth of scientific literature challenges researchers extracting and synthesizing knowledge. Traditional search engines return many sources without direct, detailed answers, while general-purpose LLMs may offer concise responses that lack depth or omit current information. LLMs with search capabilities are also limited by context window, yielding short, incomplete answers. This paper introduces WISE (Workflow for Intelligent Scientific Knowledge Extraction), a system addressing…

Improving ASP-based ORS Schedules through Machine Learning Predictions
Pierangela Bruno, Carmine Dodaro, Giuseppe Galat\`a, Marco Maratea, Marco Mochi
https://arxiv.org/abs/2507.16454

Improving ASP-based ORS Schedules through Machine Learning Predictions
The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different department units. Recently, solutions to this problem based on Answer Set Programming (ASP) have been delivered. Such solutions are overall satisfying but, when applied to real data…

Impacts of Intrinsic Noise and Quantum Entanglement on the Geometric and Dynamical Properties of the XXZ Heisenberg Interacting Spin Model
M. Yachi, B. Amghar, J. Elfakir, M. El Falaki, S. A. Chelloug, A. A Abd El-Latif, A. Slaoui
https://arxiv.org/abs/2507.17452

Impacts of Intrinsic Noise and Quantum Entanglement on the Geometric and Dynamical Properties of the XXZ Heisenberg Interacting Spin Model
Understanding how intrinsic decoherence affects the interplay between geometry, dynamics, and entanglement in quantum systems is a central challenge in quantum information science. In this work, we develop a unified framework that explores this interplay for a pair of interacting spins governed by an XXZ-type Heisenberg model under an external magnetic field and intrinsic decoherence. We quantify entanglement using the concurrence measure and examine its evolution under decoherence, revealing t…

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Ori Press, Brandon Amos, Haoyu Zhao, Yikai Wu, Samuel K. Ainsworth, Dominik Krupke, Patrick Kidger, Touqir Sajed, Bartolomeo Stellato, Jisun Park, Nathanael Bosch, Eli Meril, Albert Steppi, Arman Zharmagambetov, Fangzhao Zhang, David Perez-Pineiro, Alberto Mercurio, Ni Zhan, Talor Abramovich, Kilian Lieret, Hanlin Zhang, Shirley Huang, Matthias Bethge, Ofir Press

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Despite progress in language model (LM) capabilities, evaluations have thus far focused on models' performance on tasks that humans have previously solved, including in programming (Jimenez et al., 2024) and mathematics (Glazer et al., 2024). We therefore propose testing models' ability to design and implement algorithms in an open-ended benchmark: We task LMs with writing code that efficiently solves computationally challenging problems in computer science, physics, and mathematics. Our AlgoTu…

MyGO: Make your Goals Obvious, Avoiding Semantic Confusion in Prostate Cancer Lesion Region Segmentation
Zhengcheng Lin (City University of Macau), Zuobin Ying (City University of Macau), Zhenyu Li (Shandong University), Zhenyu Liu (Chinese Academy of Sciences), Jian Lu (Peking University), Weiping Ding (Nantong University)
https://

MyGO: Make your Goals Obvious, Avoiding Semantic Confusion in Prostate Cancer Lesion Region Segmentation
Early diagnosis and accurate identification of lesion location and progression in prostate cancer (PCa) are critical for assisting clinicians in formulating effective treatment strategies. However, due to the high semantic homogeneity between lesion and non-lesion areas, existing medical image segmentation methods often struggle to accurately comprehend lesion semantics, resulting in the problem of semantic confusion. To address this challenge, we propose a novel Pixel Anchor Module, which guid…

Never Come Up Empty: Adaptive HyDE Retrieval for Improving LLM Developer Support
Fangjian Lei, Mariam El Mezouar, Shayan Noei, Ying Zou
https://arxiv.org/abs/2507.16754

Never Come Up Empty: Adaptive HyDE Retrieval for Improving LLM Developer Support
Large Language Models (LLMs) have shown promise in assisting developers with code-related questions; however, LLMs carry the risk of generating unreliable answers. To address this, Retrieval-Augmented Generation (RAG) has been proposed to reduce the unreliability (i.e., hallucinations) of LLMs. However, designing effective pipelines remains challenging due to numerous design choices. In this paper, we construct a retrieval corpus of over 3 million Java and Python related Stack Overflow posts wi…