arXiv AI & Jobs Research (Haritz Puerto, Haonan Li, Xudong Han, Timothy Bald) • Published: 2026-02-27
AI agents powered by reasoning models require access to sensitive user data. However, their reasoning traces are difficult to control, which can result in the unintended leakage of private information to external parties. We propose training models to follow instructions not only in the final answer, but also in reasoning traces, potentially under
arXiv AI & Jobs Research (Mohsen Tajgardan, Atena Shiranzaei, Mahdi Rabbani,) • Published: 2026-02-27
Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model. By eliminating the need to transmit raw data, FL inherently preserves privacy. However, the heterogeneous natu
arXiv AI & Jobs Research (Daniel S. Berman, Brian Merritt, Stanley Ta, Dana ) • Published: 2026-02-27
At present, there are no easily understood explainable artificial intelligence (AI) methods for discrete token inputs, like text. Most explainable AI techniques do not extend well to token sequences, where both local and global features matter, because state-of-the-art models, like transformers, tend to focus on global connections. Therefore, exist
arXiv AI & Jobs Research (Zhizhou He, Yang Luo, Xinkai Liu, Mahdi Boloursaz ) • Published: 2026-02-27
Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structur
arXiv AI & Jobs Research (Luca Di Bella, Jan Bürger, Markus Demleitner, Tors) • Published: 2026-02-27
This workshop report from "Shaping the Digital Future of ErUM Research: Sustainability & Ethics" (Aachen, 2025) reviews progress on sustainability measures in data-intensive ErUM-Data research since the 2023 call-to-action on resource-aware research. It evaluates short-, medium-, and long-term actions around monitoring and reducing CO2 emissions, i
arXiv AI & Jobs Research (Abhishek Kulkarni, Sharon Lynn Chu) • Published: 2026-02-27
Interest-based learning (IBL) is a paradigm of instruction in which educational content is contextualized using learners' interests to enhance content relevance. IBL has been shown to result in improved learning outcomes. Unfortunately, high effort is needed for instructors to design and deliver IBL content for individual students. LLMs in the form
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