Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Lingrui Mei, Jiayu Yao, Yuyao Ge +12 authors
Context Engineering systematically optimizes information payloads for Large Language Models, addressing gaps in generating sophisticated, long-form outputs.
Zhiyu Li, Shichao Song, Chenyang Xi +36 authors
MemOS, a memory operating system for Large Language Models, addresses memory management challenges by unifying plaintext, activation-based, and parameter-level memories, enabling efficient storage, retrieval, and continual learning.
Xufang Luo, Yuge Zhang, Zhiyuan He +5 authors
Agent Lightning is a flexible RL framework for training LLMs in various agents, using a hierarchical RL algorithm and decoupling execution from training to handle complex interactions.
Yuqing Li, Jiangnan Li, Zheng Lin +5 authors
MiA-RAG enhances long-context reasoning in LLM-based RAG systems by incorporating hierarchical summarization to create global semantic representations that guide both retrieval and generation processes.
Yangning Li, Weizhi Zhang, Yuyao Yang +17 authors
This survey integrates reasoning and retrieval in Large Language Models to improve factuality and multi-step inference, highlighting Synergized RAG-Reasoning frameworks and outlining future research directions.
Zirui Guo, Xubin Ren, Lingrui Xu +2 authors
RAG-Anything is a unified framework that enhances multimodal knowledge retrieval by integrating cross-modal relationships and semantic matching, outperforming existing methods on complex benchmarks.
Akari Asai, Zeqiu Wu, Yizhong Wang +2 authors
Self-Reflective Retrieval-Augmented Generation (Self-RAG) enhances large language models by adaptively retrieving and reflecting on passages, improving factuality and performance across various tasks.
Boyong Wu, Chao Yan, Chen Hu +106 authors
Step-Audio~2, an end-to-end multi-modal large language model, integrates latent audio encoding and reinforcement learning to achieve state-of-the-art performance in ASR, audio understanding, and speech conversation, incorporating discrete audio token generation and retrieval-augmented generation.
Soyeong Jeong, Kangsan Kim, Jinheon Baek +1 authors
VideoRAG dynamically retrieves relevant videos based on queries and utilizes both visual and textual information for output generation, outperforming existing methods in response accuracy.
Jiejun Tan, Zhicheng Dou, Wen Wang +3 authors
HtmlRAG enhances Retrieval-Augmented Generation (RAG) systems by using HTML instead of plain text, improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning.
Prateek Chhikara, Dev Khant, Saket Aryan +2 authors
Mem0, a memory-centric architecture with graph-based memory, enhances long-term conversational coherence in LLMs by efficiently extracting, consolidating, and retrieving information, outperforming existing memory systems in terms of accuracy and computational efficiency.
Woongyeong Yeo, Kangsan Kim, Soyeong Jeong +2 authors
UniversalRAG enhances Retrieval-Augmented Generation by dynamically selecting and retrieving knowledge from various heterogeneous and modality-specific sources to improve factual accuracy across different types of queries.
Niklas Muennighoff, Hongjin Su, Liang Wang +5 authors
GrIT allows large language models to excel at both generative and embedding tasks through task instruction, leading to new state-of-the-art performance without sacrificing efficiency.
Yuri Kuratov, Aydar Bulatov, Petr Anokhin +4 authors
BABILong benchmark evaluates large language models' reasoning across long documents, revealing that models use only a small portion of context and that recurrent memory transformers handle longer contexts better.
Zhaorun Chen, Zhen Xiang, Chaowei Xiao +2 authors
AgentPoison, a novel red teaming approach, introduces a backdoor attack targeting LLM agents by poisoning their memory or knowledge base, ensuring high attack success rates with minimal impact on normal performance.
Zhuoqun Li, Xuanang Chen, Haiyang Yu +7 authors
StructRAG enhances LLMs by identifying optimal structure types, reconstructing documents into these structures, and inferring answers for knowledge-intensive reasoning tasks, achieving state-of-the-art performance.
Xiao Yang, Kai Sun, Hao Xin +24 authors
The Comprehensive RAG Benchmark (CRAG) evaluates RAG solutions with diverse QA tasks, revealing gaps in LLM accuracy and future research directions.
Tiannan Wang, Jiamin Chen, Qingrui Jia +43 authors
Weaver, a family of specialized large language models, achieves superior writing capabilities through pre-training and fine-tuning methods, surpasses GPT-4 in various writing tasks, and supports retrieval-augmented generation and tool usage.
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