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 ...

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 ...

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 ...

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 ...

In the evolving landscape of artificial intelligence, integrating vision and language capabilities remains a complex challenge. Traditional models often struggle with tasks requiring a nuanced ...

The field of artificial intelligence is evolving rapidly, with increasing efforts to develop more capable and efficient language models. However, scaling these models comes with challenges, ...

With the current conversation about widespread LLMs in AI, it is crucial to understand some of the basics involved. Despite their general-purpose pretraining in developing LLMs, most require ...

Multimodal large language models (MLLMs) have emerged as a promising approach towards artificial general intelligence, integrating diverse sensing signals into a unified framework. However, MLLMs face ...

With the release of DeepSeek R1, there is a buzz in the AI community. The open-source model offers some best-in-class performance across many metrics, even at par with state-of-the-art proprietary ...

Multimodal AI integrates diverse data formats, such as text and images, to create systems capable of accurately understanding and generating content. By bridging textual and visual data, these models ...

Large language models (LLMs) have shown remarkable abilities in language tasks and reasoning, but their capacity for autonomous planning—especially in complex, multi-step scenarios—remains limited.

Artificial intelligence has grown significantly with the integration of vision and language, allowing systems to interpret and generate information across multiple data modalities. This capability ...