-
Qwen Researchers Proposes QwenLong-L1: A Reinforcement Learning Framework for Long-Context Reasoning in Large Language Models
Qwen Researchers Propose QwenLong-L1: A Reinforcement Learning Framework for Long-Context Reasoning in Large Language Models While large reasoning models (LRMs) have shown impressive capabilities in short-context reasoning through reinforcement learning (RL), these gains do not generalize well to long-context scenarios. Applications such as multi-document QA, research synthesis, and legal or financial analysis require models to… →
-
Why Belonging Matters More Than Just Diversity
Patrick George/Ikon Images Creative workforces, accelerated business growth, and leaders who have learned how to harness their team’s diversity are all expected outcomes of well-executed diversity, equity, and inclusion (DEI) initiatives. Hiring to develop a more diverse workforce, however, doesn’t mean that these synergies will automatically accrue. Inclusive leaders need to understand that implementing DEI… →
-
Researchers at UT Austin Introduce Panda: A Foundation Model for Nonlinear Dynamics Pretrained on 20,000 Chaotic ODE Discovered via Evolutionary Search
Researchers at UT Austin Introduce Panda: A Foundation Model for Nonlinear Dynamics Pretrained on 20,000 Chaotic ODEs Discovered via Evolutionary Search Chaotic systems, including fluid dynamics and brain activity, demonstrate high sensitivity to initial conditions, complicating long-term predictions. Small errors in modeling can escalate quickly, limiting the efficacy of many scientific machine learning (SciML) approaches.… →
-
This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks
Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks Neural networks are powerful tools for tackling complex data-driven tasks. However, they often encounter difficulties when making discrete decisions under strict constraints, such as routing vehicles or scheduling jobs. These discrete decision problems, prevalent in operations research, are computationally… →
-
The Impact of Internet Hospital Follow-Up on the Quality of Life of Patients With Epilepsy: Randomized Controlled Trial
CONCLUSIONS: Long-term follow-up through IHs can effectively improve the quality of life and reduce anxiety and depression in patients with epilepsy. This model provides effective support, making it an important tool for managing epilepsy. Therefore, IH management is recommended as a feasible approach for epilepsy follow-up in clinical practice. →
-
Direct-Access Physiotherapy to Improve Access to Quality Care for Children and Adolescents Presenting to the Pediatric Emergency Department With Musculoskeletal Problems: The PEDPT-MSK Pilot Randomized Control Trial
OBJECTIVE: This pilot study aimed to evaluate the feasibility of conducting a randomized controlled trial (RCT) comparing direct-access physiotherapy for children and adolescents presenting to the pediatric emergency department (ED) with low acuity musculoskeletal complaints, to current usual care provided by a physician alone. DESIGN: Pragmatic parallel 2-arm, single-blinded, single site, feasibility pilot RCT. METHODS:… →
-
Effects of Esketamine Versus Remifentanil on Hemodynamics and Prognosis in Patients with Septic Shock Receiving Invasive Mechanical Ventilation: A Randomized Controlled Trial
CONCLUSION: Esketamine may decrease the dosage of norepinephrine in patients with septic shock receiving invasive mechanical ventilation. It is beneficial for stabilizing hemodynamics and appears to be an effective and safe agent for patients with septic shock requiring invasive mechanical ventilation. →
-
Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model
CONCLUSIONS: This interpretable LGBM-based model provides a reliable, noninvasive tool for TPE diagnosis. It supports clinical decision-making with real-time risk assessment and promises broader applicability through future integration into clinical information systems. →
-
Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better Alignment
Can LLMs Really Judge with Reasoning? Introduction Recent advancements in large language models (LLMs) have brought attention to their capabilities in reasoning and judgment. Researchers from Microsoft and Tsinghua University have introduced Reward Reasoning Models (RRMs), which aim to enhance the alignment of LLMs through dynamic scaling of computational resources during test-time evaluations. The Role… →