Tootfinder

RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams
Andrei Vlad Man, R\u{a}zvan-Alexandru Sm\u{a}du, Cristian-George Craciun, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
https://arxiv.org/abs/2507.19666

RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams
The intersection of AI and legal systems presents a growing need for tools that support legal education, particularly in under-resourced languages such as Romanian. In this work, we aim to evaluate the capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs) in understanding and reasoning about Romanian driving law through textual and visual question-answering tasks. To facilitate this, we introduce RoD-TAL, a novel multimodal dataset comprising Romanian driving test quest…

Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
Ruiyang Hao, Haibao Yu, Jiaru Zhong, Chuanye Wang, Jiahao Wang, Yiming Kan, Wenxian Yang, Siqi Fan, Huilin Yin, Jianing Qiu, Yao Mu, Jiankai Sun, Li Chen, Walter Zimmer, Dandan Zhang, Shanghang Zhang, Mac Schwager, Wei Huang, Xiaobo Zhang, Ping Luo, Zaiqing Nie
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Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. To facilitate research in this…

IMF slashes Russia's growth forecast, says war-driven economy to 'slow down': https://benborges.xyz/2025/07/30/imf-slashes-russias-growth-forecast.html

Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices
Leo Guo, Adwait Inamdar, Willem D. van Driel, GuoQi Zhang
https://arxiv.org/abs/2507.19663

Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices
Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, the usage of efficient data-driven design schemes is a popular choice. Among them, Bayesian optimization (BO) with Gaussian process regression is one of the most important representatives. The authors argue that computational savings can be …

Self-driving technologies need the help of the public: A narrative review of the evidence
Jonathan Smith, Siddartha Khastgir
https://arxiv.org/abs/2505.23472

Self-driving technologies need the help of the public: A narrative review of the evidence
If public trust is lot in a new technology early in its life cycle it can take much more time for the benefits of that technology to be realised. Eventually tens-of-millions of people will collectively have the power to determine self-driving technology success of failure driven by their perception of risk, data handling, safety, governance, accountability, benefits to their life and more. This paper reviews the evidence on safety critical technology covering trust, engagement, and acceptance. …

Modern applications require search capabilities that go beyond basic text matching. They must be fast, accurate, personalised and context-aware. At this year's Berlin Buzzwords, Saurabh Singh will demonstrate how OpenSearch’s latest AI/ML enhancements and engine improvements enable organisations to build intelligent, scalable search experiences that meet these evolving needs.
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From Search to Insight: Leveraging OpenSearch for Scalable, AI-Driven Search Experiences
Observability, log analytics, GenAI systems, and RAG pipelines must query massive volumes of semantic embeddings to retrieve relevant content instantly. Today’s search systems often fall short in handling high-dimensional vector data and similarity search. OpenSearch 3.0 brings significant architectural improvements to address these challenges. Integrating Apache Lucene 10 and JVM 21, the platform delivers 20% faster queries than its 2.x predecessor and 10× the throughput of 1.x versions. Ne…

Y’all in your SUVs need to calm down… it’s Sunday, there’s no need to white knuckle angry speed around, Morrisons won’t run out of broccoli!
Totally unpleasant experience 1/10 would not recommend.
#Driving

Data-driven quantum Koopman method for simulating nonlinear dynamics
Baoyang Zhang, Zhen Lu, Yaomin Zhao, Yue Yang
https://arxiv.org/abs/2507.21890 https://

Data-driven quantum Koopman method for simulating nonlinear dynamics
Quantum computation offers potential exponential speedups for simulating certain physical systems, but its application to nonlinear dynamics is inherently constrained by the requirement of unitary evolution. We propose the quantum Koopman method (QKM), a data-driven framework that bridges this gap through transforming nonlinear dynamics into linear unitary evolution in higher-dimensional observable spaces. Leveraging the Koopman operator theory to achieve a global linearization, our approach ma…

Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
Joydeep Chandra, Satyam Kumar Navneet
https://arxiv.org/abs/2507.20014 https://…

Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU…

Intention-Driven Generation of Project-Specific Test Cases
Binhang Qi, Yun Lin, Xinyi Weng, Yuhuan Huang, Chenyan Liu, Hailong Sun, Jin Song Dong
https://arxiv.org/abs/2507.20619

Intention-Driven Generation of Project-Specific Test Cases
Test cases are valuable assets for maintaining software quality. While numerous automated techniques have been proposed for generating tests (either by maximizing code coverage or by translating focal code into test code), practical tests are seldom driven by coverage alone. In real projects, each test reflects a developer's validation intention for a specific behaviour and embodies rich, project-specific knowledge: which specific APIs to call and what assertions truly matter. Without consideri…