4 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…
Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval
Zheng Liu, Chaofan Li, Shitao Xiao +2
Dense retrieval calls for discriminative embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs)…
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…