Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for
enhancing large language models (LLMs) with external knowledge. It leverages graphs to …

Large language models (LLMs) augmented with retrieval systems have demonstrated significant
potential in handling knowledge-intensive tasks. However, these models often struggle …

Memory emerges as the core module in the Large Language Model (LLM)-based agents for
long-horizon complex tasks (eg, multi-turn dialogue, game playing, scientific discovery), …

The rapid advancement of Large Language Models (LLMs) has empowered autonomous
agents with advanced reasoning, planning, and tool-use capabilities. However, traditional …

Retrieval-Augmented Generation (RAG) has become an essential method for mitigating
hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although …

Test-Time Training offers a promising way to improve the reasoning ability of large language
models (LLMs) by adapting the model using only the test questions. However, existing …

Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document
retrieval by structuring knowledge as relational graphs, enabling more coherent and effective …

Large language models inevitably retain sensitive information, defined as inputs that may
induce harmful generations, due to training on massive web corpora, raising concerns for …

Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training
reasoning-oriented large language models, but existing methods largely assume high-resource …

Multimodal keyphrase generation (MKP) aims to extract a concise set of keyphrases that
capture the essential meaning of paired image–text inputs, enabling structured understanding, …