Tootfinder

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung
https://arxiv.org/abs/2510.05664 …

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To …

(Soundtrack of the Mighty Mouse theme, but somehow janky: “Here I come to save the day!”)
Thankfully, the United States has only ever had good experiences deploying troops to the Middle East.
🤦‍♂️
https://apnews.com/article/mideast-gaz

US sending about 200 troops to monitor Gaza ceasefire deal
The United States is sending about 200 troops to Israel to help support and monitor the ceasefire deal in Gaza as part of a team that includes partner nations, nongovernmental organizations and private-sector players. That's according to U.S. officials who spoke on condition of anonymity to discuss details that weren't authorized for release. They said Thursday that U.S. Central Command is going to establish a “civil-military coordination center” in Israel that will help facilitate the flow…

A look at Middle East 24, a new project from right-wing X news aggregator Visegršd 24 that posts articles from anonymous authors and relies on crowdfunding (Ivan L. Nagy/Columbia Journalism Review)
https://www.cjr.org/analysis/gaza-rage-machine-po…

A new Gaza rage machine—with Polish origins.
A site called Visegrád 24 rode the waves of military conflicts and social media angst to become a household name on X. Its new project, Middle East 24, is aiming to do the same.

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues
Krish Patel, Dingkun Zhou, Ajay Kankipati, Akshaj Gupta, Zeyi Austin Li, Mohul Shukla, Vibhor Narang, Sara Kofman, Zongli Ye, Grace Wang, Xiaoyu Shi, Tingle Li, Guan-Ting Lin, Kan Jen Cheng, Huang-Cheng Chou, Jiachen Lian, Gopala Anumanchipalli
https://

AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues
Emotions conveyed through voice and face shape engagement and context in human-AI interaction. Despite rapid progress in omni-modal large language models (LLMs), the holistic evaluation of emotional reasoning with audiovisual cues remains limited. To address this gap, we introduce AV-EMO-Reasoning, a benchmark designed to systematically assess emotional coherence in LLMs. The framework leverages a curated, single- and multi-turn synthetic audiovisual corpus with a real-world set and is assessed…

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Austin Feng, Andreas Varvarigos, Ioannis Panitsas, Daniela Fernandez, Jinbiao Wei, Yuwei Guo, Jialin Chen, Ali Maatouk, Leandros Tassiulas, Rex Ying
https://arxiv.org/abs/2510.06063

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as weather, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets are underrepresented in public benchmarks due to proprietary restrictions. Existing datasets are often anonymized and normalized, removing scale information and limi…

TravelBench : Exploring LLM Performance in Low-Resource Domains
Srinivas Billa, Xiaonan Jing
https://arxiv.org/abs/2510.02719 https://arxiv.org/pdf/2510.02…

TravelBench : Exploring LLM Performance in Low-Resource Domains
Results on existing LLM benchmarks capture little information over the model capabilities in low-resource tasks, making it difficult to develop effective solutions in these domains. To address these challenges, we curated 14 travel-domain datasets spanning 7 common NLP tasks using anonymised data from real-world scenarios, and analysed the performance across LLMs. We report on the accuracy, scaling behaviour, and reasoning capabilities of LLMs in a variety of tasks. Our results confirm that gen…

Advancing Conversational AI with Shona Slang: A Dataset and Hybrid Model for Digital Inclusion
Happymore Masoka
https://arxiv.org/abs/2509.14249 https://ar…

Advancing Conversational AI with Shona Slang: A Dataset and Hybrid Model for Digital Inclusion
African languages remain underrepresented in natural language processing (NLP), with most corpora limited to formal registers that fail to capture the vibrancy of everyday communication. This work addresses this gap for Shona, a Bantu language spoken in Zimbabwe and Zambia, by introducing a novel Shona--English slang dataset curated from anonymized social media conversations. The dataset is annotated for intent, sentiment, dialogue acts, code-mixing, and tone, and is publicly available at https…