This page shows the most relevant public items for Agentic AI, 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 6, 2026 • arxiv.org • Hailong Li, Feifei Li, Wenhui Que, Xingyu Fan
Large language models (LLMs) power many interactive systems such as chatbots, customer-service agents, and personal assistants. In knowledge-intensive scenarios requiring user-specific personalizat...
Paper • Jan 6, 2026 • arxiv.org • Dongming Jiang, Yi Li, Guanpeng Li, Bingzhe Li
Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic m...
Paper • Jan 20, 2026 • arxiv.org • Dehao Tao, Guoliang Ma, Yongfeng Huang, Minghu Jiang
Human-agent dialogues often exhibit topic continuity-a stable thematic frame that evolves through temporally adjacent exchanges-yet most large language model (LLM) agent memory systems fail to pres...
Paper • Jan 8, 2026 • arxiv.org • Chengyuan Yang, Zequn Sun, Wei Wei, Wei Hu
Memory management is vital for LLM agents to handle long-term interaction and personalization. Most research focuses on how to organize and use memory summary, but often overlooks the initial memor...
Paper • Jan 8, 2026 • arxiv.org • Muzhao Tian, Zisu Huang, Xiaohua Wang, Jingwen Xu, Zhengkang Guo, Qi Qian, Yuanzhe Shen, Kaitao Song, Jiakang Yuan, Changze Lv, Xiaoqing Zheng
As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining stylistic consistency. However, most existing systems...
Paper • Jan 9, 2026 • arxiv.org • Yu Liu, Wenxiao Zhang, Cong Cao, Wenxuan Lu, Fangfang Yuan, Diandian Guo, Kun Peng, Qiang Sun, Kaiyan Zhang, Yanbing Liu, Jin B. Hong, Bowen Zhou, Zhiyuan Ma
Answering real-world open-domain multi-hop questions over massive corpora is a critical challenge in Retrieval-Augmented Generation (RAG) systems. Recent research employs reinforcement learning (RL...
Paper • Jan 10, 2026 • arxiv.org • Sergii Voloshyn
Retrieval-Augmented Generation (RAG) has emerged as the predominant paradigm for grounding Large Language Model outputs in factual knowledge, effectively mitigating hallucinations. However, convent...
Paper • Jan 9, 2026 • arxiv.org • Yue Zhou, Xiaobo Guo, Belhassen Bayar, Srinivasan H. Sengamedu
Long-term conversational agents face a fundamental scalability challenge as interactions extend over time: repeatedly processing entire conversation histories becomes computationally prohibitive. C...
Paper • Jan 11, 2026 • arxiv.org • Zili Wei, Xiaocui Yang, Yilin Wang, Zihan Wang, Weidong Bao, Shi Feng, Daling Wang, Yifei Zhang
Triple-based Iterative Retrieval-Augmented Generation (iRAG) mitigates document-level noise for multi-hop question answering. However, existing methods still face limitations: (i) greedy single-pat...
Paper • Jan 11, 2026 • arxiv.org • Hua Ye, Siyuan Chen, Ziqi Zhong, Canran Xiao, Haoliang Zhang, Yuhan Wu, Fei Shen
Large language models (LLMs) equipped with retrieval--the Retrieval-Augmented Generation (RAG) paradigm--should combine their parametric knowledge with external evidence, yet in practice they often...
Paper • Jan 12, 2026 • arxiv.org • Manzong Huang, Chenyang Bu, Yi He, Xingrui Zhuo, Xindong Wu
Graph-based Retrieval-Augmented Generation (GraphRAG) mitigates hallucinations in Large Language Models (LLMs) by grounding them in structured knowledge. However, current GraphRAG methods are const...
Paper • Jan 12, 2026 • arxiv.org • Nikhil Verma
Large Language Model (LLM) agents struggle with long-horizon software engineering tasks due to "Context Bloat." As interaction history grows, computational costs explode, latency increases,...
Paper • Jan 12, 2026 • arxiv.org • Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization. However, existing methods fail to properly mode...
Paper • Jan 12, 2026 • arxiv.org • Sirui Liang, Pengfei Cao, Jian Zhao, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu
Large language model (LLM) agents increasingly rely on accumulated memory to solve long-horizon decision-making tasks. However, most existing approaches store memory in fixed representations and re...
Paper • Jan 13, 2026 • arxiv.org • Anxin Tian, Yiming Li, Xing Li, Hui-Ling Zhen, Lei Chen, Xianzhi Yu, Zhenhua Dong, Mingxuan Yuan
Agentic memory systems have become critical for enabling LLM agents to maintain long-term context and retrieve relevant information efficiently. However, existing memory frameworks suffer from a fu...
Paper • Jan 13, 2026 • arxiv.org • Giulio Corallo, Paolo Papotti
Retrieval Augmented Generation faces a trade-off: concatenating documents in a long prompt enables multi-document reasoning but creates prefill bottlenecks, while encoding document KV caches separa...
Paper • Jan 14, 2026 • arxiv.org • Rubing Chen, Jian Wang, Wenjie Li, Xiao-Yong Wei, Qing Li
Current context augmentation methods, such as retrieval-augmented generation, are essential for solving knowledge-intensive reasoning tasks. However, they typically adhere to a rigid, brute-force s...
Paper • Jan 13, 2026 • arxiv.org • Manideep Reddy Chinthareddy
Retrieval-Augmented Generation for software engineering often relies on vector similarity search, which captures topical similarity but can fail on multi-hop architectural reasoning such as control...
Paper • Jan 24, 2026 • arxiv.org • Fengran Mo, Zhan Su, Yuchen Hui, Jinghan Zhang, Jia Ao Sun, Zheyuan Liu, Chao Zhang, Tetsuya Sakai, Jian-Yun Nie
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated ...
Paper • Jan 27, 2026 • arxiv.org • Yupeng Huo, Yaxi Lu, Zhong Zhang, Haotian Chen, Yankai Lin
Equipping agents with memory is essential for solving real-world long-horizon problems. However, most existing agent memory mechanisms rely on static and hand-crafted workflows. This limits the per...
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