Neurogene is looking to make the leap from a clinical biotech to a commercial biopharma with its lead gene therapy and has hired an industry veteran to take the reins.
Theramex exits self-regulatory body after ‘systemic’ compliance failures
Theramex has opted out of the U.K.’s self-regulatory program for pharma marketing after being found to have brought discredit on the industry twice in quick succession.
The association of transformer-based sentiment analysis with symptom distress and deterioration in routine psychotherapy care
Sentiment analysis has been of long-standing interest in psychotherapy research. Recently, the Transformer deep learning architecture has produced text-based sentiment analysis models that are highly accurate and context-aware. These models have been explored as proxies for emotion measurement instruments in psychotherapy, but not investigated as stand-alone psychometric tools. Using proposed utterance-level and session-level sentiment features […]
Measuring and reducing surgical staff stress in a realistic operating room setting using EDA monitoring and smart hearing protection
BackgroundStress is a critical factor in the operating room (OR) and affects both the performance and well-being of surgical staff. Measuring and mitigating this stress can therefore improve patient safety and healthcare worker health.ObjectiveThis study aimed to evaluate the stress levels of OR staff in a simulated surgical setting using electrodermal activity (EDA) and to […]
Bayer falls short in bid to block J&J’s survival claims in prostate cancer clash
In a blow to Bayer, a U.S. federal judge in Manhattan has said he will not block Johnson & Johnson’s survival claims on a key prostate cancer drug.
Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation
arXiv:2604.15482v1 Announce Type: cross Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning demands satisfying multiple challenging objectives simultaneously: removing undesirable knowledge, preserving general utility, avoiding over-refusal of neighboring concepts, and, crucially, ensuring robustness against adversarial probing attacks. However, existing unlearning methods primarily focus […]
From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text
arXiv:2604.16270v1 Announce Type: cross Abstract: The complexity of Vietnam’s legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solution for legal text simplification, evaluating their true capabilities requires a multifaceted approach that goes beyond surface-level metrics. This paper introduces a comprehensive dual-aspect evaluation framework to address […]
Seeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data
arXiv:2604.15374v1 Announce Type: new Abstract: Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art […]
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
arXiv:2604.16232v1 Announce Type: cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and transferability beyond black-box models. We introduce Latent Grammar Flow (LGF), a neuro-symbolic generative framework for discovering ordinary differential equations from data. LGF embeds equations as grammar-based representations into a discrete latent space […]
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
arXiv:2604.15937v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these tasks remains poorly understood: which biases are robust across providers and platforms, and which can be mitigated through prompt design. We present a controlled simulation study mapping […]
AgentV-RL: Scaling Reward Modeling with Agentic Verifier
arXiv:2604.16004v1 Announce Type: cross Abstract: Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error propagation from incorrect intermediate reasoning can lead to false positives for seemingly plausible solutions, while lacking external grounding makes verifiers unreliable on computation or knowledge-intensive tasks. To address these […]
From Vulnerable Data Subjects to Vulnerabilizing Data Practices: Navigating the Protection Paradox in AI-Based Analyses of Platformized Lives
arXiv:2604.15990v1 Announce Type: cross Abstract: This paper traces a conceptual shift from understanding vulnerability as a static, essentialized property of data subjects to examining how it is actively enacted through data practices. Unlike reflexive ethical frameworks focused on missing or counter-data, we address the condition of abundance inherent to platformized life-a context where a near […]