3 citations · 7 across the 13 of their papers we have counts for
8 papers · 1 filter
General Agentic Memory Via Deep Research
B. Y. Yan, Chaofan Li, Hongjin Qian +2
Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To a…
Retro*: Optimizing LLMs for Reasoning-Intensive Document Retrieval
Junwei Lan, Jianlyu Chen, Zheng Liu +3
With the growing popularity of LLM agents and RAG, it has become increasingly important to retrieve documents that are essential for solving a task, even when their connection to t…
ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval
Jianlyu Chen, Junwei Lan, Chaofan Li +2
In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. Fir…
OmniGen2: Towards Instruction-Aligned Multimodal Generation
Chenyuan Wu, Pengfei Zheng, Ruiran Yan +19
In this work, we introduce OmniGen2, a versatile and open-source generative model designed to provide a unified solution for diverse generation tasks, including text-to-image, imag…
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
Chaofan Li, Jianlyu Chen, Yingxia Shao +2
Code embedding models attract increasing attention due to the widespread popularity of retrieval-augmented generation (RAG) in software development. These models are expected to ca…
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG
Yubin Hong, Chaofan Li, Jingyi Zhang +1
Retrieval-Augmented Generation (RAG) enables large language models to provide more precise and pertinent responses by incorporating external knowledge. In the Query-Focused Summari…
Reinforced Information Retrieval
Chaofan Li, Zheng Liu, Jianlyv Chen +2
While retrieval techniques are widely used in practice, they still face significant challenges in cross-domain scenarios. Recently, generation-augmented methods have emerged as a p…
Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width
Zheng Liu, Chaofan Li, Shitao Xiao +3
Large language models (LLMs) provide powerful foundations to perform fine-grained text re-ranking. However, they are often prohibitive in reality due to constraints on computation…