This study examines how generative artificial intelligence (GAI) is adopted and integrated into the newsroom of a major Brazilian media organization—Globo. While AI deployment in journalism has expanded globally, the experiences lived by journalists in contexts that are not Western, educated, industrialized, rich, and democratic remain underexplored. Combining in-depth interviews, internal documents, organizational presentations, public materials, and direct observation within a qualitative case study, this study analyzes how GAI is reshaping newsroom practices, routines, and professional identities. It introduces a framework for classifying uncertainties in AI-driven newsrooms, a type II theory, which is designed to describe, classify, and organize the main forms of uncertainty faced by journalists when interacting with GAI systems, and then structure these uncertainties into three analytical levels—macro (ecosystem), meso (organization), and micro (identity)—while pairing each level with its associated coping strategies. Given its single-case basis, the framework is presented as a possible framework whose analytical categories require further empirical application before broader validation.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
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MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
