1 citations · 2 across the 12 of their papers we have counts for
11 papers · 1 filter
Writer-R1: Enhancing Generative Writing in LLMs via Memory-augmented Replay Policy Optimization
Jihao Zhao, Shuaishuai Zu, Zhiyuan Ji +2
As a typical open-ended generation task, creative writing lacks verifiable reference answers, which has long constrained reward modeling and automatic evaluation due to high human…
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
Jihao Zhao, Daixuan Li, Pengfei Li +3
The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowl…
MemEmo: Evaluating Emotion in Memory Systems of Agents
Peng Liu, Zhen Tao, Jihao Zhao +5
Memory systems address the challenge of context loss in Large Language Model during prolonged interactions. However, compared to human cognition, the efficacy of these systems in p…
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…