This page shows the most relevant public items for AI Engineering, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jan 15, 2026 • arxiv.org • Alex Dantart, Marco Kóvacs-Navarro
In enterprise datasets, documents are rarely pure. They are not just text, nor just numbers; they are a complex amalgam of narrative and structure. Current Retrieval-Augmented Generation (RAG) syst...
Paper • Jan 15, 2026 • arxiv.org • Amir Khurshid, Abhishek Sehgal
Large language model (LLM) contexts are typically constructed using retrieval-augmented generation (RAG), which involves ranking and selecting the top-k passages. The approach causes fragmentation ...
Paper • Jan 15, 2026 • arxiv.org • Ruozhen Yang, Yucheng Jiang, Yueqi Jiang, Priyanka Kargupta, Yunyi Zhang, Jiawei Han
Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under different latent goals and constraints, causing memory...
Paper • Jan 29, 2026 • arxiv.org • Yuejie Li, Ke Yang, Tao Wang, Bolin Chen, Bowen Li, Chengjun Mao
Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often chall...
Paper • Jan 17, 2026 • arxiv.org • Raquib Bin Yousuf, Shengzhe Xu, Mandar Sharma, Andrew Neeser, Chris Latimer, Naren Ramakrishnan
Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured and repetitive corp...
Paper • Jan 25, 2026 • arxiv.org • Tianyi Yang, Nashrah Haque, Vaishnave Jonnalagadda, Yuya Jeremy Ong, Zhehui Chen, Yanzhao Wu, Lei Yu, Divyesh Jadav, Wenqi Wei
Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the quality of responses in Question-Answering (QA) tasks. However, existing approaches often struggle with re...
Paper • Jan 19, 2026 • arxiv.org • Laura Dietz, Bryan Li, Gabrielle Liu, Jia-Huei Ju, Eugene Yang, Dawn Lawrie, William Walden, James Mayfield
RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System that preserves e...
Paper • Jan 22, 2026 • arxiv.org • Yuxin Yang, Gangda Deng, Ömer Faruk Akgül, Nima Chitsazan, Yash Govilkar, Akasha Tigalappanavara, Shi-Xiong Zhang, Sambit Sahu, Viktor Prasanna
Retrieval-Augmented Generation (RAG) grounds large language model outputs in external evidence, but remains challenged on multi-hop question answering that requires long reasoning. Recent works sca...
Paper • Jan 23, 2026 • arxiv.org • Haotian Chen, Qingqing Long, Siyu Pu, Xiao Luo, Wei Ju, Meng Xiao, Yuanchun Zhou, Jianghua Zhao, Xuezhi Wang
With the rapid growth of scientific literature, scientific question answering (SciQA) has become increasingly critical for exploring and utilizing scientific knowledge. Retrieval-Augmented Generati...
Paper • Jan 24, 2026 • arxiv.org • Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xinrui Bao, Xu You, Xueqing Shi, Yuhang Zhou, Heng Qi
Retrieval-augmented generation (RAG) grounds large language models with external evidence, but under a limited context budget, the key challenge is deciding which retrieved passages should be injec...
Paper • Jan 26, 2026 • arxiv.org • Seonho An, Chaejeong Hyun, Min-Soo Kim
Existing Graph RAG methods aiming for insightful retrieval on corpus graphs typically rely on time-intensive processes that interleave Large Language Model (LLM) reasoning. To enable time-efficient...
Paper • Feb 6, 2026 • arxiv.org • Lei Wei, Xiao Peng, Xu Dong, Niantao Xie, Bin Wang
Large language models deployed as autonomous agents face critical memory limitations, lacking selective forgetting mechanisms that lead to either catastrophic forgetting at context boundaries or in...
Paper • Jan 26, 2026 • arxiv.org • Yanming Liu, Xinyue Peng, Zixuan Yan, Yanxin Shen, Wenjie Xu, Yuefeng Huang, Xinyi Wang, Jiannan Cao, Jianwei Yin, Xuhong Zhang
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic exploration of external ...
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...
It ranks public content for AI Engineering using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to AI Engineering topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to AI Engineering topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use the linked entities section to jump to labs, companies, and experts connected to this topic and explore their timelines. This guidance is specific to AI Engineering topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Yes. Use the follow button on this page to subscribe and track new high-signal activity. This guidance is specific to AI Engineering topic page on Attendemia and is written so it still makes sense without reading other sections on the page.