The advancement of artificial intelligence (AI) and machine learning (ML) has enabled transformative progress across diverse fields. However, the "system domain," which focuses on optimizing and ...

Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as objects, text, and ...

Academic paper search represents a critical yet intricate information retrieval challenge within research ecosystems. Researchers require complex search capabilities that can navigate complex, ...

Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as objects, text, and ...

Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as objects, text, and ...

Reinforcement learning (RL) focuses on enabling agents to learn optimal behaviors through reward-based training mechanisms. These methods have empowered systems to tackle increasingly complex tasks, ...

As the adoption of generative AI continues to expand, developers face mounting challenges in building and deploying robust applications. The complexity of managing diverse infrastructure, ensuring ...

Multimodal large language models (MLLMs) bridge vision and language, enabling effective interpretation of visual content. However, achieving precise and scalable region-level comprehension for static ...

Agentic AI systems are fundamentally reshaping how tasks are automated, and goals are achieved in various domains. These systems are distinct from conventional AI tools in that they can adaptively ...

As the adoption of generative AI continues to expand, developers face mounting challenges in building and deploying robust applications. The complexity of managing diverse infrastructure, ensuring ...

The design and deployment of modern RLMs pose a lot of challenges. They are expensive to develop, have proprietary restrictions, and have complex architectures that limit their access. Moreover, the ...

Aligning large language models (LLMs) with human values is essential as these models become central to various societal functions. A significant challenge arises when model parameters cannot be ...