BackgroundInadequate health literacy hinders positive health outcomes, yet medical literature often exceeds the general population’s comprehension level. While health authorities recommend patient materials be at a sixth-grade reading level, scientific articles typically require college-level proficiency. Large language models (LLMs) like ChatGPT show potential for simplifying complex text, possibly bridging this gap.ObjectiveThis study evaluated the effectiveness […]
Tubulis snags $361M for clinical-stage ADCs
Tubulis is headed to one of the leading cancer R&D conferences with the fifth-largest private biotech fundraise of 2025. The Munich-based ADC biotech will present its first clinical data at the European Society for Medical …
Africa Remains Top Global Target, Even as Attacks Decline
Organizations across the continent saw 10% fewer attacks in September, but Africa remains the most attacked region in the world, leading the Global South.
Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees
arXiv:2510.12209v1 Announce Type: cross Abstract: Learning with noisy labels remains challenging because over-parameterized networks memorize corrupted supervision. Meta-learning-based sample reweighting mitigates this by using a small clean subset to guide training, yet its behavior and training dynamics lack theoretical understanding. We provide a rigorous theoretical analysis of meta-reweighting under label noise and show that its […]
Human-in-the-Loop Bandwidth Estimation for Quality of Experience Optimization in Real-Time Video Communication
arXiv:2510.12265v1 Announce Type: cross Abstract: The quality of experience (QoE) delivered by video conferencing systems is significantly influenced by accurately estimating the time-varying available bandwidth between the sender and receiver. Bandwidth estimation for real-time communications remains an open challenge due to rapidly evolving network architectures, increasingly complex protocol stacks, and the difficulty of defining QoE […]
Hierarchical Alignment: Surgical Fine-Tuning via Functional Layer Specialization in Large Language Models
arXiv:2510.12044v1 Announce Type: cross Abstract: Existing alignment techniques for Large Language Models (LLMs), such as Direct Preference Optimization (DPO), typically treat the model as a monolithic entity, applying uniform optimization pressure across all layers. This approach overlooks the functional specialization within the Transformer architecture, where different layers are known to handle distinct tasks from syntax […]
Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models
arXiv:2510.12110v1 Announce Type: cross Abstract: The evaluation of LLMs’ creativity represents a crucial research domain, though challenges such as data contamination and costly human assessments often impede progress. Drawing inspiration from human creativity assessment, we propose PACE, asking LLMs to generate Parallel Association Chains to Evaluate their creativity. PACE minimizes the risk of data contamination […]
Countermind: A Multi-Layered Security Architecture for Large Language Models
arXiv:2510.11837v1 Announce Type: cross Abstract: The security of Large Language Model (LLM) applications is fundamentally challenged by “form-first” attacks like prompt injection and jailbreaking, where malicious instructions are embedded within user inputs. Conventional defenses, which rely on post hoc output filtering, are often brittle and fail to address the root cause: the model’s inability to […]
Direct Multi-Token Decoding
arXiv:2510.11958v1 Announce Type: cross Abstract: Decoder-only transformers have become the standard architecture for large language models (LLMs) due to their strong performance. Recent studies suggest that, in pre-trained LLMs, early, middle, and late layers may serve distinct roles: Early layers focus on understanding the input context, middle layers handle task-specific processing, and late layers convert […]
HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation
arXiv:2510.10177v2 Announce Type: replace-cross Abstract: In pose estimation for seen objects, a prevalent pipeline involves using neural networks to predict dense 3D coordinates of the object surface on 2D images, which are then used to establish dense 2D-3D correspondences. However, current methods primarily focus on more efficient encoding techniques to improve the precision of predicted […]
ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery
arXiv:2508.20996v2 Announce Type: replace Abstract: Substance use disorders (SUDs) affect millions of people, and relapses are common, requiring multi-session treatments. Access to care is limited, which contributes to the challenge of recovery support. We present textbfChatThero, an innovative low-cost, multi-session, stressor-aware, and memory-persistent autonomous emphlanguage agent designed to facilitate long-term behavior change and therapeutic support […]
DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
arXiv:2510.12796v1 Announce Type: cross Abstract: Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a “supervision deficit”: the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized. To remedy this, we propose textbfDriveVLA-W0, […]