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GURU: A Reinforcement Learning Framework that Bridges LLM Reasoning Across Six Domains

Limitations of Reinforcement Learning in Narrow Reasoning Domains Reinforcement Learning RL has demonstrated strong potential to enhance the reasoning capabilities of LLMs, particularly in leading systems such as OpenAI-O3 and DeepSeek-R1. However, most RL research has focused narrowly on math…

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Build a Powerful Multi-Tool AI Agent Using Nebius with Llama 3 and Real-Time Reasoning Tools

In this tutorial, we introduce an advanced AI agent built using Nebius’ robust ecosystem, particularly the ChatNebius, NebiusEmbeddings, and NebiusRetriever components. The agent utilizes the Llama-3.3-70B-Instruct-fast model to generate high-quality responses, incorporating external functionalities such as Wikipedia search, contextual document…

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Inception Labs Introduces Mercury: A Diffusion-Based Language Model for Ultra-Fast Code Generation

Generative AI and Its Challenges in Autoregressive Code Generation The field of generative artificial intelligence has significantly impacted software development by automating various coding tasks, ranging from simple auto-completions to complex software solutions. However, traditional language models predominantly employ autoregressive…

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MIT and NUS Researchers Introduce MEM1: A Memory-Efficient Framework for Long-Horizon Language Agents

Modern language agents need to handle multi-turn conversations, retrieving and updating information as tasks evolve. However, most current systems simply add all past interactions to the prompt, regardless of relevance. This leads to bloated memory usage, slower performance, and poor…

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Google DeepMind Releases AlphaGenome: A Deep Learning Model that can more Comprehensively Predict the Impact of Single Variants or Mutations in DNA

A Unified Deep Learning Model to Understand the Genome Google DeepMind has unveiled AlphaGenome, a new deep learning framework designed to predict the regulatory consequences of DNA sequence variations across a wide spectrum of biological modalities. AlphaGenome stands out by…

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ETH and Stanford Researchers Introduce MIRIAD: A 5.8M Pair Dataset to Improve LLM Accuracy in Medical AI

Challenges of LLMs in Medical Decision-Making: Addressing Hallucinations via Knowledge Retrieval LLMs are set to revolutionize healthcare through intelligent decision support and adaptable chat-based assistants. However, a major challenge is their tendency to produce factually incorrect medical information. To address…

Read MoreETH and Stanford Researchers Introduce MIRIAD: A 5.8M Pair Dataset to Improve LLM Accuracy in Medical AI