One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of model capabilities. This is ...

Graphical User Interface (GUI) agents are crucial in automating interactions within digital environments, similar to how humans operate software using keyboards, mice, or touchscreens. GUI agents can ...

In the ever-evolving world of artificial intelligence (AI), large language models have proven instrumental in addressing a wide array of challenges, from automating complex... The field of multimodal ...

Retrieval-augmented generation (RAG) has become a key technique in enhancing the capabilities of LLMs by incorporating external knowledge into their outputs. RAG methods enable LLMs to access ...

In the ever-evolving world of artificial intelligence (AI), large language models have proven instrumental in addressing a wide array of challenges, from automating complex... Addressing the ...

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents' capabilities in ML engineering. Existing coding ...

Transformer architecture has enabled large language models (LLMs) to perform complex natural language understanding and generation tasks. At the core of the Transformer is an attention mechanism ...

Multimodal Attributed Graphs (MMAGs) have received little attention despite their versatility in image generation. MMAGs represent relationships between entities with combinatorial complexity in a ...

Multimodal foundation models, like GPT-4 and Gemini, are effective tools for a variety of applications because they can handle data formats other than text, such as images. However, these models are ...

Addressing the Challenges in AI Development The journey to building open source and collaborative AI has faced numerous challenges. One major problem is the centralization... The field of multimodal ...