This page shows the most relevant public items for RAG, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Feb 5, 2026 • arxiv.org • Mahdi Astaraki, Mohammad Arshi Saloot, Ali Shiraee Kasmaee, Hamidreza Mahyar, Soheila Samiee
Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-reasoning loops meaningfully outperform static RAG,...
Paper • Feb 2, 2026 • arxiv.org • Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin
The rapid evolution of Large Language Model (LLM) agents has necessitated robust memory systems to support cohesive long-term interaction and complex reasoning. Benefiting from the strong capabilit...
Paper • Jan 28, 2026 • arxiv.org • Vishnu Sashank Dorbala, Dinesh Manocha
Foundation models rely on in-context learning for personalized decision making. The limited size of this context window necessitates memory compression and retrieval systems like RAG. These systems...
Paper • Jan 29, 2026 • arxiv.org • Jiate Liu, Zebin Chen, Shaobo Qiao, Mingchen Ju, Danting Zhang, Bocheng Han, Shuyue Yu, Xin Shu, Jingling Wu, Dong Wen, Xin Cao, Guanfeng Liu, Zhengyi Yang
Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure. However, practic...
Paper • Jan 29, 2026 • arxiv.org • Wei Wen, Sihang Deng, Tianjun Wei, Keyu Chen, Ruizhi Qiao, Xing Sun
Query optimization is a crucial component for the efficacy of Retrieval-Augmented Generation (RAG) systems. While reinforcement learning (RL)-based agentic and reasoning methods have recently emerg...
Paper • Jan 29, 2026 • arxiv.org • Yang Zhao, Chengxiao Dai, Yue Xiu, Mengying Kou, Yuliang Zheng, Dusit Niyato
Agentic large language model (LLM) systems rely on external memory for long-horizon state and concurrent multi-agent execution, but centralized indexes and heuristic partitions become bottlenecks a...
Paper • Jan 29, 2026 • arxiv.org • Kaixiang Wang, Yidan Lin, Jiong Lou, Zhaojiacheng Zhou, Bunyod Suvonov, Jie Li
The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requires maintaining rigorous logical integrity over ex...
Paper • Jan 29, 2026 • arxiv.org • Zhao Wang, Ziliang Zhao, Zhicheng Dou
Reinforcement learning (RL) has become a promising paradigm for optimizing Retrieval-Augmented Generation (RAG) in complex reasoning tasks. However, traditional outcome-based RL approaches often su...
Paper • Jan 29, 2026 • arxiv.org • Yiqun Chen, Erhan Zhang, Tianyi Hu, Shijie Wang, Zixuan Yang, Meizhi Zhong, Xiaochi Wei, Yan Gao, Yi Wu, Yao Hu, Jiaxin Mao
The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reasoning. However, e...
Paper • Jan 30, 2026 • arxiv.org • Tianyi Hu, Niket Tandon, Akhil Arora
Existing retrieval-augmented generation (RAG) systems are primarily designed under the assumption that each query has a single correct answer. This overlooks common information-seeking scenarios wi...
Paper • Feb 3, 2026 • arxiv.org • Haojia Zhu, Qinyuan Xu, Haoyu Li, Yuxi Liu, Hanchen Qiu, Jiaoyan Chen, Jiahui Jin
Aggregation query over free text is a long-standing yet underexplored problem. Unlike ordinary question answering, aggregate queries require exhaustive evidence collection and systems are required ...
Paper • Feb 2, 2026 • arxiv.org • Qirui Mi, Zhijian Ma, Mengyue Yang, Haoxuan Li, Yisen Wang, Haifeng Zhang, Jun Wang
LLM-driven agents demonstrate strong performance in sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. This insufficient experienc...
Paper • Feb 2, 2026 • arxiv.org • Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen
Standard Operating Procedures (SOPs) are essential for ensuring operational safety and consistency in industrial environments. However, retrieving and following these procedures presents unique cha...
Paper • Feb 5, 2026 • arxiv.org • Konosuke Yoshizato, Kazuma Shimizu, Ryota Higa, Takanobu Otsuka
This study investigates large language model (LLM) -based multi-agent systems (MASs) as a promising approach to inventory management, which is a key component of supply chain management. Although t...
Paper • Feb 5, 2026 • arxiv.org • Chang Yang, Chuang Zhou, Yilin Xiao, Su Dong, Luyao Zhuang, Yujing Zhang, Zhu Wang, Zijin Hong, Zheng Yuan, Zhishang Xiang, Shengyuan Chen, Huachi Zhou, Qinggang Zhang, Ninghao Liu, Jinsong Su, Xinrun Wang, Yi Chang, Xiao Huang
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), where memory can ena...
Paper • Feb 5, 2026 • arxiv.org • Taoye Yin, Haoyuan Hu, Yaxin Fan, Xinhao Chen, Xinya Wu, Kai Deng, Kezun Zhang, Feng Wang
In financial Retrieval-Augmented Generation (RAG) systems, models frequently rely on retrieved documents to generate accurate responses due to the time-sensitive nature of the financial domain. Whi...
Paper • Feb 5, 2026 • arxiv.org • Hao Yang, Zhiyu Yang, Xupeng Zhang, Wei Wei, Yunjie Zhang, Lin Yang
Retrieval-augmented generation (RAG) has become a key paradigm for knowledge-intensive question answering. However, existing multi-hop RAG systems remain inefficient, as they alternate between retr...
Paper • Feb 5, 2026 • arxiv.org • Joseph Fioresi, Parth Parag Kulkarni, Ashmal Vayani, Song Wang, Mubarak Shah
Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results. To improve robustness and solution quality, recent appr...
Paper • Feb 5, 2026 • arxiv.org • Haozhen Zhang, Haodong Yue, Tao Feng, Quanyu Long, Jianzhu Bao, Bowen Jin, Weizhi Zhang, Xiao Li, Jiaxuan You, Chengwei Qin, Wenya Wang
Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can ...
Paper • Jan 4, 2026 • arxiv.org • Danial Amin
Large language models (LLMs) are increasingly deployed as autonomous decision agents in settings with asymmetric error costs: hiring (missed talent vs wasted interviews), medical triage (missed eme...
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