The computational complexity of the self-attention mechanism in popular transformer
architectures poses significant challenges for training and inference, and becomes the …

Abstract Retrieval Augmented Generation (RAG) has been a powerful tool for Large
Language Models (LLMs) to efficiently process overly lengthy contexts. However, recent …

The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted
their impressive capabilities in various applications, such as collaborative problem-solving …

Recent large language model (LLM)-driven chat assistant systems have integrated memory
components to track user-assistant chat histories, enabling more accurate and personalized …

Characterizing the express power of the Transformer architecture is critical to understanding
its capacity limits and scaling law. Recent works provide the circuit complexity bounds to …

Maritime operations play a critical role in global trade but face persistent safety challenges
due to human error, environmental factors, and operational complexities. This review …

Handling long-context inputs is crucial for large language models (LLMs) in tasks such as
extended conversations, document summarization, and many-shot in-context learning …

Current mobile assistants are limited by dependence on system APIs or struggle with
complex user instructions and diverse interfaces due to restricted comprehension and …

Humans share a wide variety of images related to their personal experiences within
conversations via instant messaging tools. However, existing works focus on (1) image …

LLM-based agents have been widely applied as personal assistants, capable of memorizing
information from user messages and responding to personal queries. However, there still …