On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

This review paper explores Multimodal Large Language Models (MLLMs), which integrate
Large Language Models (LLMs) like GPT-4 to handle multimodal data such as text and …

In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …

In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To
address this issue, a prevailing idea is to learn stable features, on the assumption that they …

The new kind of Agent-oriented information system, exemplified by GPTs, urges us to
inspect the information system infrastructure to support Agent-level information processing …

The recommendation of medication is a vital aspect of intelligent healthcare systems, as it
involves prescribing the most suitable drugs based on a patient's specific health needs …

Although large-scale pre-trained models hold great potential for adapting to downstream
tasks through fine-tuning, the performance of such fine-tuned models is often limited by the …

In this age where data is abundant, the ability to distill meaningful insights from the sea of
information is essential. Our research addresses the computational and resource …

The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly
advanced multimodal tasks in payment, security, and advertising, notably enhancing …

In the era of data proliferation, efficiently sifting through vast information to extract
meaningful insights has become increasingly crucial. This paper addresses the …