1 citations · 1 across the 3 of their papers we have counts for
7 papers
TAdaRAG: Task Adaptive Retrieval-Augmented Generation via On-the-Fly Knowledge Graph Construction
Jie Zhang, Bo Tang, Wanzi Shao +8
Retrieval-Augmented Generation (RAG) improves large language models by retrieving external knowledge, often truncated into smaller chunks due to the input context window, which lea…
MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems
Jihao Zhao, Zhiyuan Ji, Simin Niu +3
The traditional RAG paradigm, which typically engages in the comprehension of relevant text chunks in response to received queries, inherently restricts both the depth of knowledge…
MemOS: A Memory OS for AI System
Zhiyu Li, Chenyang Xi, Chunyu Li +36
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
Zhiyu Li, Shichao Song, Hanyu Wang +19
Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language…
Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering
Jihao Zhao, Chunlai Zhou, Daixuan Li +2
The collaborative paradigm of large and small language models (LMs) effectively balances performance and cost, yet its pivotal challenge lies in precisely pinpointing the moment of…
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models
Xun Liang, Hanyu Wang, Huayi Lai +7
Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottlene…