Informledge System (ILS) is a knowledge network with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. In this paper, we put forward the strategies for knowledge embedding and retrieval in an ILS. ILS is a powerful knowledge network system dealing with logical storage and connectivity of information units to form knowledge using autonomous nodes and multi-lateral links. In ILS, the autonomous nodes known as Knowledge Network Nodes (KNN)s play vital roles which are not only used in storage, parsing and in forming the multi-lateral linkages between knowledge points but also in helping the realization of intelligent retrieval of linked information units in the form of knowledge. Knowledge built in to the ILS forms the shape of sphere. The intelligence incorporated into the links of a KNN helps in retrieving various knowledge threads from a specific set of KNNs. A developed entity of information realized through KNN forms in to the shape of a knowledge cone
PMU（费萨尔国王大学）；D. S. Institutions（D. S. 教育机构）；The Oxford College of Engineering（牛津工程学院）
There has been a considerable advance in computing, to mimic the way in which the brain tries to comprehend and structure the information to retrieve meaningful knowledge. It is identified that neuronal entities hold whole of the knowledge that the species makes use of. We intended to develop a modified knowledge based system, termed as Informledge System (ILS) with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. We conceive that every piece of knowledge is a cluster of cross-linked and correlated structure. In this paper, we put forward the theory …
How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
Dept. of Computer Science, Old Dominion University（欧道明大学计算机科学系）；Virginia Modeling, Analysis, and Simulation Center, Old Dominion University（欧道明大学弗吉尼亚建模、分析与仿真中心）；Virginia Digital Maritime Center, Old Dominion University（欧道明大学弗吉尼亚数字海事中心）；Office of Enterprise Research and Innovation, Old Dominion University（欧道明大学企业研究与创新办公室）
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
RespAI Lab, KIIT Bhubaneswar（KIIT布巴内斯瓦尔 RespAI 实验室）；BITS Pilani（皮拉尼比尔拉理工学院）；University of South Florida（南佛罗里达大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Cognitive arithmetic studies the mental processes used in solving math problems. This area of research explores the retrieval mechanisms and strategies used by people during a common cognitive task. Past research has shown that human performance in arithmetic operations is correlated to the numerical size of the problem. Past research on cognitive arithmetic has pinpointed this trend to either retrieval strength, error checking, or strategy-based approaches when solving equations. This paper describes a rule-based computational model that performs the four major arithmetic operations (addition…
Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies
organization= Department of Nuclear Engineering, Hanyang University , addressline= 222 Wangsimni-ro , postcode= 04763 , state= Seongdong-gu , city= Seoul , country= South Korea；organization= The Grainger College of Engineering, Nuclear, Plasma \& Radiological Engineering, University of Illinois Urbana-Champaign , city= Urbana , state= IL , country= USA
Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations
Research Center for Information Technology Innovation, Academia Sinica（资讯科技创新研究所以）；NVIDIA（NVIDIA公司）；Department of Computer Science, National Chengchi University（国立政治大学计算机科学系）
University of Notre Dame（圣母大学）；Microsoft Cognitive Services Research（微软认知服务研究院）；Shanghai Jiaotong University（上海交通大学）
A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and relation spaces that can be modeled. However, applying such methods to commonsense reasoning tasks faces two unique challenges, i.e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever. In this paper, we systematically investigate how to leverage commonsense knowledge retrieval to improve commonsense reasoning tasks. We proposed a unified framework of retrieval…
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
