arXiv AI & Jobs Research (Shruti Bothe, Illyyne Saffar, Aurelie Boisbunon, H) • Published: 2025-11-26
The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On
arXiv AI & Jobs Research (Beth Plale, Neelesh Karthikeyan, Isuru Gamage, Joe) • Published: 2025-11-26
AI/ML model cards can contain a benchmarked evaluation of an AI/ML model against intended use but a one time assessment during model training does not get at how and where a model is actually used over its lifetime. Through Patra Model Cards embedded in the ICICLE AI Institute software ecosystem we study model cards as dynamic objects. The study re
arXiv AI & Jobs Research (Kirill Paramonov, Mete Ozay, Aristeidis Mystakidis) • Published: 2025-11-26
The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling users to correct AI misclassi
arXiv AI & Jobs Research (Daniel R. Jiang, Jalaj Bhandari, Yukai Yang, Rémi ) • Published: 2025-11-26
Optimizing large language models (LLMs) for multi-turn conversational outcomes remains a significant challenge, especially in goal-oriented settings like AI marketing or sales agents who facilitate transactions via messaging platforms. The difficulty stems from sparse, long-horizon rewards and the discrepancy between response-level planning and tok
arXiv AI & Jobs Research (Anna Marklová, Ondřej Vinš, Martina Vokáčová, Jiří) • Published: 2025-11-26
Large language models are increasingly capable of producing creative texts, yet most studies on AI-generated poetry focus on English -- a language that dominates training data. In this paper, we examine the perception of AI- and human-written Czech poetry. We ask if Czech native speakers are able to identify it and how they aesthetically judge it.
arXiv AI & Jobs Research (Hans Gundlach, Alex Fogelson, Jayson Lynch, Ana Tr) • Published: 2025-11-26
Algorithms have been estimated to increase AI training FLOP efficiency by a factor of 22,000 between 2012 and 2023 [Ho et al., 2024]. Running small-scale ablation experiments on key innovations from this time period, we are able to account for less than 10x of these gains. Surveying the broader literature, we estimate that additional innovations no
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