Tootfinder

Läsvärd kulturdebatt. https://www.dn.se/kultur/baste-benjamin-dousa-vem-har-slangt-dig-under-bussen/

Sverker Sörlin: Benjamin Dousa, vem slängde dig under bussen?
Sverker Sörlin frågar i ett öppet brev varför Sveriges biståndsminister har stoppat hjälpen till det svältande och sönderbombade Gaza.

»Nur jeder Dritte schützt sein E-Banking mit einem sicheren #Passwort:
Die meisten #Schweiz'er Konsumentinnen und Konsumenten vertrauen ihrer Bank, wenn es um den Schutz persönlicher Daten und die #Sicherheit

Sam Williams on Matt Eberflus' 'violent' defense, bouncing back in contract year https://www.dallascowboys.com/news/sam-williams-on-matt-eberflus-violent-defense-bouncing-back-in-contract-year

Sources: Visa shut down its open-banking business in the US amid regulatory uncertainty; the unit provided tools to help fintechs access customer-account data (Paige Smith/Bloomberg)
https://www.bloomberg.com/news/articles/2025-…

Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
Fred Mutisya (Qhala, Kenya Medical Association), Shikoh Gitau (Qhala), Christine Syovata (Kenya Medical Association), Diana Oigara (Kenya Medical Association), Ibrahim Matende (Kenya Medical Association), Muna Aden (Kenya Medical Association), Munira Ali (Kenya Medical Association), Ryan Nyotu (Kenya Medical Association), Diana Marion (Kenya Medical Asso…

Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
Introduction: Existing medical LLM benchmarks largely reflect examination syllabi and disease profiles from high income settings, raising questions about their validity for African deployment where malaria, HIV, TB, sickle cell disease and other neglected tropical diseases (NTDs) dominate burden and national guidelines drive care. Methodology: We systematically reviewed 31 quantitative LLM evaluation papers (Jan 2019 May 2025) identifying 19 English medical QA benchmarks. Alama Health QA was de…

Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark
Goeric Huybrechts, Srikanth Ronanki, Sai Muralidhar Jayanthi, Jack Fitzgerald, Srinivasan Veeravanallur
https://arxiv.org/abs/2507.15882

Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark
The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To address this, we introduce Document Haystack, a comprehensive benchmark designed to evaluate the performance of Vision Language Models (VLMs) on long, visually complex documents. Document Haystack features documents ra…

Benchmarking Computer Science Survey Generation
Weihang Su, Anzhe Xie, Qingyao Ai, Jianming Long, Jiaxin Mao, Ziyi Ye, Yiqun Liu
https://arxiv.org/abs/2508.15658 https://…

Benchmarking Computer Science Survey Generation
Scientific survey articles play a vital role in summarizing research progress, yet their manual creation is becoming increasingly infeasible due to the rapid growth of academic literature. While large language models (LLMs) offer promising capabilities for automating this process, progress in this area is hindered by the absence of standardized benchmarks and evaluation protocols. To address this gap, we introduce SurGE (Survey Generation Evaluation), a new benchmark for evaluating scientific s…

Sam Williams on Matt Eberflus' 'violent' defense, bouncing back in contract year https://www.dallascowboys.com/news/sam-williams-on-matt-eberflus-violent-defense-bouncing-back-in-contract-year

A Survey on Large Language Model Benchmarks
Shiwen Ni, Guhong Chen, Shuaimin Li, Xuanang Chen, Siyi Li, Bingli Wang, Qiyao Wang, Xingjian Wang, Yifan Zhang, Liyang Fan, Chengming Li, Ruifeng Xu, Le Sun, Min Yang
https://arxiv.org/abs/2508.15361

A Survey on Large Language Model Benchmarks
In recent years, with the rapid development of the depth and breadth of large language models' capabilities, various corresponding evaluation benchmarks have been emerging in increasing numbers. As a quantitative assessment tool for model performance, benchmarks are not only a core means to measure model capabilities but also a key element in guiding the direction of model development and promoting technological innovation. We systematically review the current status and development of large la…

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
Xiangyang Zhu, Yuan Tian, Chunyi Li, Kaiwei Zhang, Wei Sun, Guangtao Zhai
https://arxiv.org/abs/2508.15526

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM safety evaluation benchmarks are proposed. However, existing benchmarks generally rely on labor-intensive manual curation, which causes excessive time and resource consumption. They also exhibit significant redundancy and limited difficulty. To alleviate these problems, we introduce SafetyFlow, the first agent-flow sys…