Reasoning language models, or RLMs, are increasingly used to simulate step-by-step problem-solving by generating long, structured reasoning chains. These models […]
Category: Applications
Reinforcement Learning, Not Fine-Tuning: Nemotron-Tool-N1 Trains LLMs to Use Tools with Minimal Supervision and Maximum Generalization
Equipping LLMs with external tools or functions has become popular, showing great performance across diverse domains. Existing research depends on […]
RL^V: Unifying Reasoning and Verification in Language Models through Value-Free Reinforcement Learning
LLMs have gained outstanding reasoning capabilities through reinforcement learning (RL) on correctness rewards. Modern RL algorithms for LLMs, including GRPO, […]
OpenAI Releases HealthBench: An Open-Source Benchmark for Measuring the Performance and Safety of Large Language Models in Healthcare
OpenAI has released HealthBench, an open-source evaluation framework designed to measure the performance and safety of large language models (LLMs) […]
Multimodal AI Needs More Than Modality Support: Researchers Propose General-Level and General-Bench to Evaluate True Synergy in Generalist Models
Artificial intelligence has grown beyond language-focused systems, evolving into models capable of processing multiple input types, such as text, images, […]
Offline Video-LLMs Can Now Understand Real-Time Streams: Apple Researchers Introduce StreamBridge to Enable Multi-Turn and Proactive Video Understanding
Video-LLMs process whole pre-recorded videos at once. However, applications like robotics and autonomous driving need causal perception and interpretation of […]
AG-UI (Agent-User Interaction Protocol): An Open, Lightweight, Event-based Protocol that Standardizes How AI Agents Connect to Front-End Applications
The current generation of AI agents has made significant progress in automating backend tasks such as summarization, data migration, and […]
This AI Paper Introduces Effective State-Size (ESS): A Metric to Quantify Memory Utilization in Sequence Models for Performance Optimization
In machine learning, sequence models are designed to process data with temporal structure, such as language, time series, or signals. […]
LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for Long-Document Retrieval and Benchmark-Leading Performance
Semantic retrieval focuses on understanding the meaning behind text rather than matching keywords, allowing systems to provide results that align […]
A Coding Implementation of Accelerating Active Learning Annotation with Adala and Google Gemini
In this tutorial, we’ll learn how to leverage the Adala framework to build a modular active learning pipeline for medical […]