arXiv:2607. 00048v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in exam- and certification-style question answering tasks, where their ability to retrieve, interpret, and apply domain-specific knowledge can be systematically assessed.
By Robson Alves Vilar, Emanuel Dantas Filho, Ademar Fran\c{c}a de Sousa Neto, Mirko Perkusich, Danyllo Wagner Albuquerque, Jo\~ao Paiva, Kyller Gorg\^onio, Angelo Perkusich
arXiv:2607. 01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps.
By Aastha Sapkota, M. G. Sarwar Murshed
arXiv:2606. 17588v1 Announce Type: cross Abstract: Several studies have examined the use of large language models (LLMs) for title-abstract screening in systematic reviews (SRs), reporting mixed accuracy.
By Mika M\"antyl\"a, Patricia Matsubara, Katia Romero Felizardo, Miikka Kuutila, Marco Gerosa, Savio de Sousa Sampaio, Tayana Conte, Igor Steinmacher
arXiv:2607. 02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation.
By Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira
arXiv:2607. 14707v1 Announce Type: cross Abstract: Large language models routinely produce fluent answers to single-shot prompts, yet deploying them as reliable components of a domain decision system is substantially harder.
By Akash Raj
arXiv:2606. 18062v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistance.
By Hobin Kim, Xiaoyuan Wu, Omer Akgul, Lujo Bauer, Nicolas Christin