Tootfinder

Internal email: xAI lays off hundreds of data annotation team staffers, following a strategic shift to prioritize specialist AI tutors over generalist roles (Grace Kay/Business Insider)
https://www.businessinsider.com/elon-musk-xai-layoffs-data-annotators-2025-9

Musk's xAI just laid off hundreds of workers tasked with training Grok
XAI laid off at least 500 of the workers on its data annotation team on Friday night.

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
Cameron Morin, Matti Marttinen Larsson
https://arxiv.org/abs/2510.12306

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc…

eu_procurements: EU procurement contract networks (2008-2016)
A bipartite network of public EU procurement contracts, from 2008 to 2016, between issuing buyers (public institutions such as a ministry or city hall) and supplying winners (a private firm). Contracts are aggregated into annual snapshots, edges are annotated with contract value information. Nodes are annotated with location information, including country of origin.
This network has 839824 nodes and 4098711 edges.

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa, Marina Danilevsky
https://arxiv.org/abs/2510.11897

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs) to ensure they are faithful and don't provide misinformation. Several benchmarks have been created to measure the performance of LLMs on this task. We present a longitudinal study comparing the feedback loop of an internal and external human annotator group for the complex annotation task of creating multi-turn RAG c…

Steam Machine: Preis angeblich auf Niveau von vergleichbarem PC
Valve verbindet mit der Steam Machine PC-Funktionen mit Konsolen-Komfort. Der Preis wird nicht verraten – es gibt aber zumindest ein paar Andeutungen.

China says Nvidia was found in violation of antitrust regulations in a preliminary investigation, in a brief statement without further details (Edwin Chan/Bloomberg)
https://www.bloomberg.com/news/articles/2025-09-15/china-find…

Social opprobrium is not antithetical to free speech. It is a •pillar• of free speech. It is the premise upon which the First Amendment is built.

BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
Tomas Ruiz, Siyao Peng, Barbara Plank, Carsten Schwemmer
https://arxiv.org/abs/2510.12516

BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limited to domains with verifiably correct answers, like mathematics and coding. We transfer test-time scaling to the LeWiDi-2025 tasks to evaluate annotation disagreements. We experiment with three test-time scaling methods: two benchmark algorithms (Model Averaging and Majority Voting), and a Best-of-N sampling method. T…

Google proposes ad tech changes to address a €2.95B EU antitrust fine, including enhancing interoperability across its ad services, but still plans to appeal (Samuel Stolton/Bloomberg)
https://www.bloomberg.com/news/articles/202…

Information-Preserving Reformulation of Reasoning Traces for Antidistillation
Jiayu Ding, Lei Cui, Li Dong, Nanning Zheng, Furu Wei
https://arxiv.org/abs/2510.11545 https://

Information-Preserving Reformulation of Reasoning Traces for Antidistillation
Recent advances in Large Language Models (LLMs) show that extending the length of reasoning chains significantly improves performance on complex tasks. While revealing these reasoning traces helps users better follow, verify, and learn from the model's problem-solving process, it also makes them highly vulnerable to unauthorized distillation. To mitigate this risk, proprietary model providers often adopt aggressive protection strategies, such as replacing detailed reasoning with brief summaries…