arXiv AI & Jobs Research (Koyena Pal, David Bau, Chandan Singh) • Published: 2026-01-16
Large reasoning models (LRMs) produce a textual chain of thought (CoT) in the process of solving a problem, which serves as a potentially powerful tool to understand the problem by surfacing a human-readable, natural-language explanation. However, it is unclear whether these explanations generalize, i.e. whether they capture general patterns about
arXiv AI & Jobs Research (János Kramár, Joshua Engels, Zheng Wang, Bilal Chu) • Published: 2026-01-16
Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful systems. Prior work has shown that activation probes may be a promising misuse mitigation technique, but we identify a key remaining challenge: probes fail to generalize under important production distribut
arXiv AI & Jobs Research (Evan Dong, Nikhil Garg, Sarah Dean) • Published: 2026-01-16
Machine learning models are often used to make predictions about admissions process outcomes, such as for colleges or jobs. However, such decision processes differ substantially from the conventional machine learning paradigm. Because admissions decisions are capacity-constrained, whether a student is admitted depends on the other applicants who ap
arXiv AI & Jobs Research (Markus Bink, Marten Risius, Udo Kruschwitz, David ) • Published: 2026-01-16
Generative AI (GenAI) tools are transforming information seeking, but their fluent, authoritative responses risk overreliance and discourage independent verification and reasoning. Rather than replacing the cognitive work of users, GenAI systems should be designed to support and scaffold it. Therefore, this paper introduces an LLM-based conversatio
arXiv AI & Jobs Research (Emelie Fälton, Isabelle Strömstedt, Mathis Brossie) • Published: 2026-01-16
We present our first stage results from deploying an LLM-augmented visualization software in a classroom setting to engage primary school children with earth-related datasets. Motivated by the growing interest in conversational AI as a means to support inquiry-based learning, we investigate children's expectations, engagement, and evaluation of a s
arXiv AI & Jobs Research (Zhen Xu, Xin Guan, Chenxi Shi, Qinhao Chen, Renzhe) • Published: 2026-01-16
The growing emphasis on 21st-century competencies in postsecondary education, intensified by the transformative impact of generative AI, underscores the need to evaluate how these competencies are embedded in curricula and how effectively academic programs align with evolving workforce and societal demands. Curricular Analytics, particularly recent
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