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Littérature scientifique sur le sujet « RAG Optimization Framework »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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Articles de revues sur le sujet "RAG Optimization Framework"

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Yumuşak, Semih. "An Information-Theoretic Framework for Retrieval-Augmented Generation Systems." Electronics 14, no. 15 (2025): 2925. https://doi.org/10.3390/electronics14152925.

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Retrieval-Augmented Generation (RAG) systems have emerged as a critical approach for enhancing large language models with external knowledge, yet the field lacks systematic theoretical analysis for understanding their fundamental characteristics and optimization principles. A novel information-theoretic approach for analyzing and optimizing RAG systems is introduced in this paper by modeling them as cascading information channel systems where each component (query encoding, retrieval, context integration, and generation) functions as a distinct information-theoretic channel with measurable cap
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Vaibhav Fanindra Mahajan. "Retrieval-augmented generation: The technical foundation of intelligent AI Chatbots." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4093–99. https://doi.org/10.30574/wjarr.2025.26.1.1571.

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Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in conversational AI technology, addressing fundamental limitations of traditional chatbot systems. This technical article explores the architecture, mechanisms, and advantages of RAG implementations. Traditional AI chatbots suffer from outdated knowledge bases, hallucination tendencies, and limited context awareness - constraints that RAG effectively overcomes by combining dynamic information retrieval with sophisticated text generation capabilities. The RAG framework operates through a multi-stage process encompass
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Kwon, Mincheol, Jimin Bang, Seyoung Hwang, Junghoon Jang, and Woosin Lee. "A Dynamic-Selection-Based, Retrieval-Augmented Generation Framework: Enhancing Multi-Document Question-Answering for Commercial Applications." Electronics 14, no. 4 (2025): 659. https://doi.org/10.3390/electronics14040659.

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Commercial multi-document question-answering (QA) applications require a high multi-document retrieval performance, while simultaneously minimizing Application Programming Interface (API) usage costs of large language models (LLMs) and system complexity. To address this need, we designed the Dynamic-Selection-based, Retrieval-Augmented Generation (DS-RAG) framework, which consists of two key modules: an Entity-Preserving Question Decomposition (EPQD) module that effectively decomposes questions while preserving the entities of the original user’s question to reduce unnecessary retrieval and en
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Dhami, Aatishkumar, and Lagan Goel. "Optimizing retrieval augmented generation pipelines for domain specific applications." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 55–72. https://doi.org/10.63345/ijrmeet.org.v13.i3.4.

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Retrieval Augmented Generation (RAG) pipelines have emerged as a transformative approach in integrating external knowledge into generative models. However, tailoring these systems to domain-specific applications presents unique challenges, including the handling of specialized vocabularies and intricate contextual nuances. This paper introduces a novel optimization framework for RAG pipelines, emphasizing adaptive retrieval strategies, customized knowledge bases, and fine-tuned generative components. By incorporating domain-tailored filtering mechanisms and dynamically adjusting retrieval para
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Ievgen, Gartman. "Architectural Features of Extended Retrieval Generation with External Memory." International Journal of Engineering and Computer Science 14, no. 06 (2025): 27355–61. https://doi.org/10.18535/ijecs.v14i06.5163.

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This article examines the RoCR framework, a Retrieval-Augmented Generation (RAG) system optimized for edge deployment in latency-sensitive environments such as real-time search, product recommendation, and dynamic content generation in eCommerce platforms. RoCR leverages Compute-in-Memory (CiM) architectures to enable fast, energy-efficient inference at scale. At the core of the solution is the CiM-Retriever, a module optimized for performing max inner product search (MIPS). Two architectural variants of the generator are analyzed—decoder-only (RA-T) and encoder–decoder with kNN cross-attentio
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Researcher. "OPTIMIZING AI ALGORITHMS: AN EMPIRICAL STUDY OF FEATURE ENGINEERING, FINE-TUNING, AND EVALUATION STRATEGIES." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2183–96. https://doi.org/10.5281/zenodo.14370672.

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This comprehensive article explores cutting-edge techniques for optimizing machine learning models, with a particular focus on advanced strategies for enhancing AI algorithms and large language models (LLMs). We begin by examining the critical role of feature engineering and selection in model performance, emphasizing the importance of word embeddings in natural language processing tasks. The article then delves into hyperparameter optimization methods, including grid search, random search, and Bayesian optimization, alongside tools that automate these processes. We introduce Spectrum, a
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Sezgin, Anıl, and Aytuğ Boyacı. "Real-Time Drone Command Processing: A Large Language Model Approach for IoD Systems." Turkish Journal of Science and Technology 20, no. 1 (2025): 281–97. https://doi.org/10.55525/tjst.1623326.

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One of the most critical steps toward autonomous capabilities, where natural language instructions can be successfully converted into executable API calls, is integrating Large Language Models (LLMs) into the ecosystem of the Internet of Drones (IoD). This study introduces an end-to-end LLM-based framework for enhancing real-time drone operation and problem handling in intent recognition, parameter extraction, and ambiguity resolution. It has resorted to a spectrum of methodologies in the form of Retrieval-Augmented Generation (RAG) and customized fine-tuning specific to each domain, towards a
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Narendra Kumar Reddy Choppa and Mark Knipp. "Advancing Generative AI with GraphQL API: Unified Data Access in Microsoft Fabric Ecosystem." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 438–50. https://doi.org/10.32996/jcsts.2025.7.5.54.

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GraphQL API integration within the Microsoft Fabric ecosystem represents a transformative advancement in how organizations manage and access data across diverse data sources unified by OneLake, including Lakehouses, Data Warehouses, SQL Databases, Mirrored Databases, and Datamarts. This integration enables efficient data retrieval, optimized query processing, and seamless connectivity across the Microsoft Fabric ecosystem. This unified data access approach is particularly advantageous for generative AI applications, as it simplifies the process of gathering and integrating diverse datasets req
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Xu, Sheng. "Algorithm Optimization and Performance Improvement of Debt Enterprise Information Retrieval System in the Big Data Environment." Frontiers in Computing and Intelligent Systems 13, no. 1 (2025): 23–25. https://doi.org/10.54097/gqqkaf43.

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Against the backdrop of the big data era, the debt enterprise information retrieval system, as the core tool for financial risk management, is confronted with the challenge of processing massive heterogeneous data. The multi-source heterogeneity, high-frequency dynamics and concealed correlations of debt information lead to high data integration costs, difficult timeliness guarantee and insufficient penetration of deep risks, causing deviations in risk assessment and errors in the prediction of innovation potential. This paper reviews the existing technical solutions, deeply analyzes the chara
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Twinkle Joshi. "Architecting Agentic AI for Modern Software Testing: Capabilities, Foundations, and a Proposed Scalable Multi-Agent System for Automated Test Generation." Journal of Information Systems Engineering and Management 10, no. 52s (2025): 625–38. https://doi.org/10.52783/jisem.v10i52s.10768.

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The progression of software testing has evolved from manual processes to automated systems. However, the emergence of Agentic AI-driven testing represents the next transformative leap. These intelligent agents autonomously generate, execute, and optimize tests, redefining the quality assurance (QA) landscape. Agentic AI—defined by its capacity to independently perceive, plan, execute, and learn—has emerged as a transformative force in software testing. This article examines the impact of Agentic AI on the software testing lifecycle, highlighting its core capabilities, such as dynamic test gene
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Thèses sur le sujet "RAG Optimization Framework"

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Smith, Frank A. "A framework for flexible comparison and optimization of X-ray digital tomosynthesis." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1667.

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Digital tomosynthesis is a novel three-dimensional imaging technology that utilizes limited number of X-ray projection images to improve the diagnosis and detection of lesions. In recent years, tomosynthesis has been used in a variety of clinical applications such as dental imaging, angiography, chest imaging, bone imaging, and breast imaging. The goal of our research is to develop a framework to enable flexible optimization and comparison of image reconstruction and imaging configurations.
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Zheng, Wenjie. "A distributed Frank-Wolfe framework for trace norm minimization via the bulk synchronous parallel model." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS049/document.

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L'apprentissage des matrices de rang faible est un problème de grande importance dans les statistiques, l'apprentissage automatique, la vision par ordinateur et les systèmes de recommandation. En raison de sa nature NP-difficile, une des approches principales consiste à résoudre sa relaxation convexe la plus étroite : la minimisation de la norme de trace. Parmi les différents algorithmes capables de résoudre cette optimisation, on peut citer la méthode de Frank-Wolfe, particulièrement adaptée aux matrices de grande dimension. En préparation à l'utilisation d'infrastructures distribuées pour ac
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Zheng, Wenjie. "A distributed Frank-Wolfe framework for trace norm minimization via the bulk synchronous parallel model." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS049.

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L'apprentissage des matrices de rang faible est un problème de grande importance dans les statistiques, l'apprentissage automatique, la vision par ordinateur et les systèmes de recommandation. En raison de sa nature NP-difficile, une des approches principales consiste à résoudre sa relaxation convexe la plus étroite : la minimisation de la norme de trace. Parmi les différents algorithmes capables de résoudre cette optimisation, on peut citer la méthode de Frank-Wolfe, particulièrement adaptée aux matrices de grande dimension. En préparation à l'utilisation d'infrastructures distribuées pour ac
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Livres sur le sujet "RAG Optimization Framework"

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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research
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Chapitres de livres sur le sujet "RAG Optimization Framework"

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Istrate, Ioan-Robert, José-Luis Gálvez-Martos, and Javier Dufour. "A Life Cycle-Based Scenario Analysis Framework for Municipal Solid Waste Management." In Towards a Sustainable Future - Life Cycle Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77127-0_20.

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AbstractA framework for the systematic analysis of the material flows and the life cycle environmental performance of municipal solid waste (MSW) management scenarios is described in this article. This framework is capable of predicting the response of waste treatment processes to the changes in waste streams composition that inevitably arise in MSW management systems. The fundamental idea is that the inputs (raw materials and energy) and outputs (final products, emissions, etc.) into/from treatment processes are previously allocated to the specific waste materials contained in the input waste
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Agostinelli, Sofia, and Benedetto Nastasi. "Immersive Facility Management—A Methodological Approach Based on BIM and Mixed Reality for Training and Maintenance Operations." In The Urban Book Series. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_13.

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AbstractInnovation technology in industries including manufacturing and aerospace is moving toward the use of Mixed Reality (MR) and advanced tools while Architecture, Engineering and Construction (AEC) sector is still remaining behind it. Moreover, the use of immersive technologies in the AEC digital education, as well as for professional training, is still little considered. Augmented and Mixed Reality (AR/MR) have the capability to provide a “X-ray vision”, showing hidden objects in a virtual/real overlay. This feature in the digital object visualization is extremely valuable for improving
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Lucas, Keane, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, and Saurabh Shintre. "Deceiving ML-Based Friend-or-Foe Identification for Executables." In Advances in Information Security. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16613-6_10.

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AbstractDeceiving an adversary who may, e.g., attempt to reconnoiter a system before launching an attack, typically involves changing the system’s behavior such that it deceives the attacker while still permitting the system to perform its intended function. We develop techniques to achieve such deception by studying a proxy problem: malware detection.Researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization fram
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"Data-driven modeling and artificial intelligence approaches for optimizing physical vapor deposition of boron nitride coatings." In Book of Abstracts - RAD 2025 Conference. RAD Centre, Niš, Serbia, 2025. https://doi.org/10.21175/rad.abstr.book.2025.2.2.

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This study introduces an innovative machine learning framework designed to enhance the physical vapor deposition (PVD) process of boron nitride (BN) coatings on D2 steel substrates via magnetron sputtering. By methodically adjusting process parameters and employing a broad range of surface characterization techniques—including pin-on-disk testing, nano-indentation, and film thickness measurements—an extensive dataset is generated that integrates both experimental and synthetic data. Advanced statistical analyses explore the relationships between deposition variables and coating performance. A
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"Biological treatment plan optimization: A novel computational solution." In Book of Abstracts - RAD 2025 Conference. RAD Centre, Niš, Serbia, 2025. https://doi.org/10.21175/rad.abstr.book.2025.38.1.

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Current state of the art treatment planning allows the achievement of very complex goals under given constraints. A complex set of parameters has to be evaluated when an irradiation plan is to be assessed, instead of the conventional minimum and maximum doses figures of merit that were used two decades ago. The biological approach is based on different radiobiological indices, linking the plan to specific clinical goals. In order to enhance the plan and get values of the indices close enough to the prescribed goals, a tool to assess plans may be very helpful, so that the planner can be certain
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Kazantzi, Vasiliki, Vassilis Gerogiannis, and Leonidas Anthopoulos. "Multi-Criteria Decision Making for Supplier Selection in Biomass Supply Networks for Bioenergy Production." In Outsourcing Management for Supply Chain Operations and Logistics Service. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2008-7.ch018.

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Decision-making on outsourcing biomass supply operations for bioenergy production is both of strategic and operational importance and can be modeled as a multi-perspective supplier selection problem characterized by multiple qualitative and quantitative factors, as well as technical and non-technical attributes and constraints. The biomass supply system presents unique features that highly impact the bioenergy production. Network functionality, raw material availability (influenced by seasonality, weather/climate conditions, land suitability, and other parameters), and procurement costs consti
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Du, Dawei, and Dan Simon. "Biogeography-Based Optimization for Large Scale Combinatorial Problems." In Efficiency and Scalability Methods for Computational Intellect. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3942-3.ch010.

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Biogeography-based optimization (BBO) is a recently-developed heuristic algorithm that has shown impressive performance and efficiency over many standard benchmarks. The application of BBO is still limited because it was only developed four years ago. The objective of this chapter is to expand the application of BBO to large scale combinatorial problems. This chapter addresses the solution of combinatorial problems based on BBO combined with five techniques: (1) nearest neighbor algorithm (NNA), (2) crossover methods designed for traveling salesman problems (TSPs), (3) local optimization metho
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Sharma, Kapil Kumar, Gopal Krishna, Gaurav Singh Negi, and Jitendra Kumar Gupta. "Federated Learning-Based Frameworks for Trusted and Secure Communication in IoVs." In Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1). BENTHAM SCIENCE PUBLISHERS, 2024. https://doi.org/10.2174/9789815313031124030009.

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Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offere
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Sharma, Kapil Kumar, Gopal Krishna, Gaurav Singh Negi, and Jitendra Kumar Gupta. "Federated Learning-Based Frameworks for Trusted and Secure Communication in IoVs." In Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1). BENTHAM SCIENCE PUBLISHERS, 2024. https://doi.org/10.2174/9789815313024124030009.

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Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offere
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Koufi Vassiliki, Malamateniou Flora, and Vassilacopoulos George. "A Big Data-driven Model for the Optimization of Healthcare Processes." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-512-8-697.

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Healthcare organizations increasingly navigate a highly volatile, complex environment in which technological advancements and new healthcare delivery business models are the only constants. In their effort to out-perform in this environment, healthcare organizations need to be agile enough in order to become responsive to these increasingly changing conditions. To act with agility, healthcare organizations need to discover new ways to optimize their operations. To this end, they focus on healthcare processes that guide healthcare delivery and on the technologies that support them. Business pro
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Actes de conférences sur le sujet "RAG Optimization Framework"

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Tao, Yijie, Chathurika Ranaweera, Sampath Edirisinghe, et al. "Cross-layer Resource Optimization for Energy Minimization in Reconfigurable Optical Crosshaul Architecture." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m1i.4.

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We formulated a cross-layer optimization framework for next-generation reconfigurable optical crosshaul architecture in Radio Access Networks (RAN) that jointly optimizes optical, packet, and RAN function-layer resources to minimize energy consumption while meeting service demands.
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Chishty, Haider A., and Fabrizio Sergi. "A Multi-Objective Simulation-Optimization Framework for the Design of a Compliant Gravity Balancing Orthosis." In 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2024. http://dx.doi.org/10.1109/biorob60516.2024.10719701.

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Ahmed, Abdulhakeem, and Ana I. Torres. "Design and Optimization of Circular Economy Networks: A Case Study of Polyethylene Terephthalate (PET)." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.154237.

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Circular systems design is an emerging approach for promoting sustainable development. Despite its perceived advantages, the characterization of circular systems remains loosely defined and ambiguous. This work proposes a network optimization framework that evaluates three objective functions related to economic and environmental domains and employs a Pareto analysis to illuminate the trade-offs between objectives. The US polyethylene terephthalate (PET) value chain is selected as a case study and represented via a superstructure containing various recycling pathways. The superstructure optimi
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Bhattacharya, Saikath, Eric Spero, Vidhyashree Nagaraju, Lance Fiondella, and Anindya Ghoshal. "Process Improvement for Rotorcraft Tradespace Exploration incorporating Reliability and Availability." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11339.

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Tradespace exploration (TSE) is a Department of Defense (DOD) Engineered Resilient Systems (ERS) (Ref.1) thrust, with overarching goals to develop processes and products capable of performing in a wide range of adverse conditions commonly encountered by military systems. Combined with technology, TSE is modernizing system engineering, facilitating stakeholder value elicitation as well as distributed collaborative environments for design and analysis of alternatives. The majority of existing TSE research emphasizes tradeoffs between functional requirements, especially those pertaining to perfor
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Katsios, Gregorios, Diego Manzanas Lopez, Benjamin Ryjikov, Samuel A Merten, and Daniel A Balasubramanian. "MetaBPL: Fault Detection in Business Logic Systems." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006454.

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Critical workflows in manufacturing, infrastructure, and logistics rely heavily on business process logic systems, where even minor faults or vulnerabilities can lead to significant operational disruptions or security breaches. For large companies, a typical product recall may cost more than ten million USD, and every hour unplanned downtime of a manufacturing line might incur a million dollars in losses. While typical processes like statistical quality assurance and auditing can help mitigate future occurrences of faults, they time consuming and often lead to downtime as corrective actions ar
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Dong Wei, Huang Jingsheng, and Liu Meiyin. "An optimization method of harmonic analysis in IEC framework." In 2nd IET Renewable Power Generation Conference (RPG 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1878.

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Alameer, Alaa, and Aydin Sezgin. "Optimization framework for baseband functionality splitting in C-RAN." In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2017. http://dx.doi.org/10.1109/camsap.2017.8313118.

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Roberts, Kendric, and Yen-Lin Han. "Investigating Density Functional Theory’s Effectiveness in Studying Metal-Organic Frameworks Structures." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11013.

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Abstract In combatting human induced climate change, carbon capture provides the potential to more slowly ease away from the dependence on hydrocarbon fuel sources, while mitigating the amount of CO2 released into the atmosphere. One promising material to use is metal-organic frameworks (MOF’s). MOF’s offer an immense variety in potential exceptionally porous structures, a property important in separation. As a result of practical experimental measurements being expensive and time consuming, interest in accomplishing the same goal through modeling has also increased. Using density functional t
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Masuda, Shimpei, Ko Ayusawa, and Eiichi Yoshida. "Optimization Framework of Humanoid Walking Pattern for Human Motion Retargeting." In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). IEEE, 2018. http://dx.doi.org/10.1109/humanoids.2018.8625060.

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Carrillo Córcoles, Xavier, Jurij Sodja, and Roeland De Breuker. "OPTIMIZATION FRAMEWORK OF A RAM AIR INLET COMPOSITE MORPHING FLAP." In 10th ECCOMAS Thematic Conference on Smart Structures and Materials. Dept. of Mechanical Engineering & Aeronautics University of Patras, 2023. http://dx.doi.org/10.7712/150123.9788.444474.

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