arXiv:2607. 19209v1 Announce Type: cross Abstract: This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs).
By Valdemar \v{S}v\'abensk\'y, Jan Vykopal, Sukrit Leelaluk, Pavel \v{C}eleda, Fumiya Okubo, Atsushi Shimada
arXiv:2607. 18725v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult.
By Shaswata Mitra, Subash Neupane, Trisha Chakraborty, Himanshu Tripathi, Sudip Mittal, Aritran Piplai, Shahram Rahimi
arXiv:2510. 14113v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets.
By Matan Levi, Daniel Ohayon, Ariel Blobstein, Ravid Sagi, Ian Molloy, Yair Allouche
The study explores whether combining traditional and digital learning analytics can predict failure in a first‑year CS1 course. Using data from 284 students across four cohorts, the authors identified ten candidate factors and built a logistic regression model that achieved 74.7% accuracy and 0.742 macro F1, with 87% recall for failing students. Weighted academic momentum, basic demographics, and LMS activity emerged as the most predictive features, suggesting that simple digital markers can enable early‑warning systems by week five.
By Lighton Phiri, Mutune Chaibela, Ivy Chisha, David Pungwa, Danny Siabbaba, Bydon Simukoko
arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.
By Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha
arXiv:2602. 16346v4 Announce Type: replace-cross Abstract: LLM-based agents execute real-world workflows via tools and memory.
By Nivya Talokar, Ayush K Tarun, Murari Mandal, Maksym Andriushchenko, Antoine Bosselut