This paper introduces UAF (Unified Audio Front-end LLM), a novel large language model that unifies critical audio front-end tasks like voice activity detection, speaker recognition, and automatic speech recognition into a single end-to-end generative framework. UAF aims to overcome the limitations of traditional cascaded pipelines and enhance full-duplex speech interaction by jointly modeling semantic content and interaction-level control signals.

The paper proposes the “innovation annulus” model to explain the persistent gap between open scholarly metadata and commercially refined data, offering a welfare‑based framework for governing its optimal width.

The paper introduces an ontology‑driven dataspace to formalize test annotations for cyber‑physical energy systems, enabling reproducible and FAIR‑compliant experimentation across laboratories.

This paper introduces PREF-XAI, a novel approach for generating personalized, rule-based explanations of black-box machine learning models. It reframes explanation as a preference-driven decision problem, learning individual user preferences through robust ordinal regression to tailor explanations.

This paper introduces SAFER, a continual unlearning framework designed to address critical issues like knowledge erosion and forgetting reversal in repeated machine unlearning scenarios. It aims to maintain model utility and prevent the reactivation of forgotten information, making AI systems more reliable and privacy-compliant over their lifecycle.