Edge devices like smartphones, IoT gadgets, and embedded systems process data locally, improving privacy, reducing latency, and enhancing responsiveness, and AI is getting integrated into these ...

Language models (LMs) have significantly progressed through increased computational power during training, primarily through large-scale self-supervised pretraining. While this approach has yielded ...

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 ...

Reinforcement Learning RL trains agents to maximize rewards by interacting with an environment. Online RL alternates between taking actions, collecting observations and rewards, and updating policies ...

Large Language Models (LLMs) are primarily designed for text-based tasks, limiting their ability to interpret and generate multimodal content such as images, videos, and audio. Conventionally, ...

Ad hoc networks are decentralized, self-configuring networks where nodes communicate without fixed infrastructure. They are commonly used in military, disaster recovery, and IoT applications. Each ...

Vision-language models (VLMs) face a critical challenge in achieving robust generalization beyond their training data while maintaining computational resources and cost efficiency. Approaches, such ...

Vision-language models (VLMs) face a critical challenge in achieving robust generalization beyond their training data while maintaining computational resources and cost efficiency. Approaches, such ...

Despite recent advancements, generative video models still struggle to represent motion realistically. Many existing models focus primarily on pixel-level reconstruction, often leading to ...

Despite progress in AI-driven human animation, existing models often face limitations in motion realism, adaptability, and scalability. Many models struggle to generate fluid body movements and rely ...

The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations ...

Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement ...