Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, making them ...

Agentic AI stands at the intersection of autonomy, intelligence, and adaptability, offering solutions that can sense, reason, and act in real or virtual environments with minimal human oversight. At ...

Knowledge graphs have been used tremendously in the field of enterprise lately, with their applications realized in multiple data forms from legal persons to registered capital and shareholder’s ...

Yandex, a global tech company, develops and open-sources Perforator, an innovative tool for continuous real-time monitoring and analysis of servers and applications. Perforator helps developers ...

The critical issue of restricted access to high-quality reasoning datasets has limited open-source AI-driven logical and mathematical reasoning advancements. While proprietary models have leveraged ...

Tokenization plays a fundamental role in the performance and scalability of Large Language Models (LLMs). Despite being a critical component, its influence on model training and efficiency remains ...

Large Language Models (LLMs) have emerged as transformative tools in research and industry, with their performance directly correlating to model size. However, training these massive models presents ...

Vision-Language Models (VLMs) have significantly expanded AI’s ability to process multimodal information, yet they face persistent challenges. Proprietary models such as GPT-4V and Gemini-1.5-Pro ...

Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...

LLMs based on transformer architectures, such as GPT and LLaMA series, have excelled in NLP tasks due to their extensive parameterization and large training datasets. However, research indicates that ...

Large Language Models (LLMs) have emerged as transformative tools in research and industry, with their performance directly correlating to model size. However, training these massive models presents ...

For example, when a user asks a question, the LLM analyzes the input and decides whether it can answer directly or if additional steps (like a web search) are needed.