Real-time speech translation presents a complex challenge, requiring seamless integration of speech recognition, machine translation, and text-to-speech synthesis. Traditional cascaded approaches ...

Time series forecasting presents a fundamental challenge due to its intrinsic non-determinism, making it difficult to predict future values accurately. Traditional methods generally employ point ...

Large language models (LLMs) are the foundation for multi-agent systems, allowing multiple AI agents to collaborate, communicate, and solve problems. These agents use LLMs to understand tasks, ...

In this tutorial, we demonstrate how to efficiently fine-tune the Llama-2 7B Chat model for Python code generation using advanced techniques such as QLoRA, gradient checkpointing, and supervised ...

The International Mathematical Olympiad (IMO) is a globally recognized competition that challenges high school students with complex mathematical problems. Among its four categories, geometry stands ...

Deep-Research is an iterative research agent that autonomously generates search queries, scrapes websites, and processes information using AI reasoning models. It aims to provide a structured approach ...

As deep learning models continue to grow, the quantization of machine learning models becomes essential, and the need for effective compression techniques has become increasingly relevant. Low-bit ...

Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in ...

Large language models (LLMs) must align with human preferences like helpfulness and harmlessness, but traditional alignment methods require costly retraining and struggle with dynamic or conflicting ...

LLM inference is highly resource-intensive, requiring substantial memory and computational power. To address this, various model parallelism strategies distribute workloads across multiple GPUs, ...

Code generation models have made remarkable progress through increased computational power and improved training data quality. State-of-the-art models like Code-Llama, Qwen2.5-Coder, and ...

Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it ...