The CHMP recommended eight new medicines and 13 line extensions at its May meeting, including Novo Nordisk’s oral weight loss therapy.
Patient and clinician perceptions, expectations, and usability of ankle exoskeletons for daily living: a mixed-methods survey study
Ankle exoskeletons offer promising support for individuals with chronic foot drop, yet user and clinician perspectives on their use in daily living remain underexplored. Related studies on lower limb exoskeletons have typically assessed user perceptions following direct physical interaction with devices, which may influence feedback and its interpretation. In contrast, this study aimed to assess […]
Development of reconfigurable smart medical wards using integrated components and complex features
Patient treatment in hospitals requires their regular monitoring to assess their health conditions. At the same time, routine measurements are often delayed, missed, or not analyzed, and this situation worsens at night and on weekends due to reduced staffing, which in turn affects the quality of treatment. Innovations in the field of infocommunication technologies allow […]
A maturity model framework for federated networks of trusted research environments
IntroductionA Trusted Research Environment (TRE) is a highly secure computer system where sensitive data is stored that researchers can access remotely and make use of in a safe setting. TREs can form federated networks when they have overlapping objectives, such as providing data access to the same researchers for the same project. However, there is […]
Enhancing Deep Neural Network Reliability with Refinement and Calibration
arXiv:2605.23249v1 Announce Type: cross Abstract: Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This has motivated research on calibrated models, where calibration measures how well a model’s predicted confidence aligns with the empirical probability of correctness. However, calibration metrics can often […]
Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
arXiv:2605.23138v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome […]
Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning
arXiv:2605.23171v1 Announce Type: cross Abstract: Recent advancements in instructional fine-tuning have injected noise into embeddings, with NEFTune (Jain et al., 2024) setting benchmarks using uniform noise. Despite NEFTune’s empirical findings that uniform noise outperforms Gaussian noise, the reasons for this remain unclear. This paper aims to clarify this by offering a thorough analysis, both theoretical […]
DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods
arXiv:2605.23052v1 Announce Type: cross Abstract: We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our system addresses three tasks: psychological state modeling, temporal change detection, and sequence-level summarization. For Task 1, we combine LLM-based data augmentation, DeBERTa classification, and Random Forest regression for […]
Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
arXiv:2605.23089v1 Announce Type: cross Abstract: Model-based reinforcement learning improves sample efficiency by learning a world model. However, existing latent world models such as DreamerV3 do not explicitly enforce local smoothness in their learned transition dynamics, leaving a useful inductive bias for transition dynamics learning unexploited. We propose GPLD, a gradient-penalized latent dynamics regularizer for DreamerV3 […]
A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism
arXiv:2605.22993v1 Announce Type: cross Abstract: Characteristic linguistic behaviors associated with Social Language Disorder (SLD) in autism spectrum disorder, including echoic repetition, pronoun displacement, and stereotyped media quoting, are largely absent from spontaneous conversation and only emerge under specific conversational conditions. In structured clinical assessments, this latency means that questioning strategy selection is a critical yet […]
Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning
arXiv:2605.23315v1 Announce Type: cross Abstract: Large language models trained under diverse objectives and architectures have been shown to develop increasingly similar internal representations, an observation formalized as the Platonic Representation Hypothesis. Whether this representational convergence extends to the reasoning processes that operate over shared representations remains untested. We evaluate representational similarity across 16 language models […]
Uncovering the Latent Potential of Deep Intermediate Representations
arXiv:2605.23033v1 Announce Type: cross Abstract: Foundational Models pretrained on huge amount of data learn representations that evolve across depth, forming a hierarchy of embeddings with distinct semantic content and geometric structure. Contrary to the widespread practice of using only the final layer or shallow mixtures, we show that task-relevant information is distributed non-monotonically across layers […]