Scientific literature search is an information retrieval (IR) task in which ranked lists are insufficient: a researcher entering a new area needs to know not only which papers are relevant, but how they relate, where they overlap, how they differ, and what problem-method combinations are absent. Standard retrieval-augmented generation (RAG) summarizes documents independently, discarding this comparative signal. We present the Novelty-Aware Research Agent, a prototype agentic retrieval system that layers structured multi-step reasoning on a RAG pipeline through six typed-contract components: query analysis, a ReAct-style retrieval loop, relevance ranking, schema-guided contribution extraction, a three-pass comparison agent, and answer generation. Beyond returning relevant papers, it produces structured comparison artifacts: per-paper contribution records, paper-level overlaps, and a problem x method gap matrix. On a 100-paper corpus, the system supports five structured comparison capabilities that a standard RAG baseline supports none of, while remaining query-sensitive: across three main queries no paper appears in all three top-5 sets (mean pairwise Jaccard 0.12), and an extended seven-query evaluation holds the pattern across ten queries (mean Jaccard 0.115, 18 of 29 retrieved papers query-exclusive). Under author-assigned graded relevance the ranker attains mean Precision@5 1.000 and nDCG@5 0.752 on the main queries, ahead of BM25, dense, and hybrid retrieval; over ten queries Precision@5 is non-saturated at 0.980 with nDCG@5 0.739. Schema compliance is 86.7% on the main queries and 84.0% over the ten-query set, and validating 20 sampled empty gap-matrix cells yields a gap precision of 0.600. We discuss the latency-structure trade-off in agentic retrieval and identify corpus scale, author-assigned labels, and limited independent evaluation as the main limitations.
CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；State Key Lab of General AI, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能全国重点实验室）；SKLCCSE, School of Computer Science and Engineering, Beihang University, China（北京航空航天大学计算机科学与工程学院）
LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning
The Hong Kong University of Science and Technology（香港科技大学）；Huawei Hong Kong Research Center（华为香港研究中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；WeBank Co., Ltd（微众银行）
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Georgia（佐治亚大学）；South China University of Technology（华南理工大学）；Technical University of Munich, University of Cologne（慕尼黑技术大学、科隆大学）；SenseTime Research（商汤科技研究院）；Qingyuan Research Institute, Shanghai Jiaotong University（青原研究院，上海交通大学）
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Cleveland State University（克利夫兰州立大学）；Kent State University（肯特州立大学）；Northeastern University（东北大学）；Roux Institute at Northeastern University（东北大学鲁克斯研究所）；University of Agder (UiA)（阿格德尔大学）
University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）；Lanzhou University（兰州大学）；University of Glasgow（格拉斯哥大学）；City University of Hong Kong（香港城市大学）；The Ohio State University（俄亥俄州立大学）
SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
University of North Dakota（北达科他大学）；Youngstown State University（青年州大学）；University of Toledo（托莱多大学）；University of Missouri（密苏里大学）；Tribhuvan University（特里布文大学）
Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction
Texas A&M University（德克萨斯A&M大学）；University of Waterloo（滑铁卢大学）；UC San Diego（圣地亚哥大学）；Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Verdent AI；Lambda
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis
Computational Social Science ETH Zürich（计算社会科学 ETH 瑞士 Zurich）；Computational Social Science, ETH Complexity Science Hub Zürich（计算社会科学 ETH 复杂性科学中心 Zurich）；ETH Complexity Science Hub Zürich, Switzerland（ETH 复杂性科学中心 Zurich 瑞士）；Vienna, Austria（维也纳 奥地利）
MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System
Department of Industrial Data Engineering, Hanyang University, Republic of Korea（工业数据工程系，翰阳大学）；School of Interdisciplinary Industrial Studies, Hanyang University, Republic of Korea（跨学科工业研究学院，翰阳大学）
