69 articles tagged with #rag. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AIBearisharXiv – CS AI · Mar 266/10
🧠Research reveals that Retrieval-Augmented Generation (RAG) systems exhibit fairness issues, with queries from certain demographic groups systematically receiving higher accuracy than others. The study identifies three key factors affecting fairness: group exposure in retrieved documents, utility of group-specific documents, and attribution bias in how generators use different group documents.
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AINeutralarXiv – CS AI · Mar 266/10
🧠A research study on retrieval-augmented generation (RAG) systems for AI policy analysis found that improving retrieval quality doesn't necessarily lead to better question-answering performance. The research used 947 AI policy documents and discovered that stronger retrieval can paradoxically cause more confident hallucinations when relevant information is missing.
AINeutralarXiv – CS AI · Mar 176/10
🧠Researchers have developed a new AI framework that uses citation-enforced retrieval-augmented generation (RAG) specifically for analyzing tax and fiscal documents. The system prioritizes transparency and explainability for tax authorities, showing improved citation accuracy and reduced AI hallucinations when tested on real IRS documents.
AINeutralarXiv – CS AI · Mar 176/10
🧠Research reveals that LLM query rewriting in RAG systems shows highly domain-dependent performance, degrading retrieval effectiveness by 9% in financial domains while improving it by 5.1% in scientific contexts. The study identifies that effectiveness depends on whether rewriting improves or worsens lexical alignment between queries and domain-specific terminology.
AIBullisharXiv – CS AI · Mar 176/10
🧠GlobalRAG is a new reinforcement learning framework that significantly improves multi-hop question answering by decomposing questions into subgoals and coordinating retrieval with reasoning. The system achieves 14.2% average improvements in performance metrics while using only 42% of the training data required by baseline models.
AIBullisharXiv – CS AI · Mar 166/10
🧠Researchers improved agentic Retrieval-Augmented Generation (RAG) systems by introducing contextualization and de-duplication modules to address inefficiencies in complex question-answering. The enhanced Search-R1 pipeline achieved 5.6% better accuracy and 10.5% fewer retrieval turns using GPT-4.1-mini.
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AIBullisharXiv – CS AI · Mar 116/10
🧠Researchers propose TaSR-RAG, a new framework that improves Retrieval-Augmented Generation systems by using taxonomy-guided structured reasoning for better evidence selection. The system decomposes complex questions into triple sub-queries and performs step-wise evidence matching, achieving up to 14% performance improvements over existing RAG baselines on multi-hop question answering benchmarks.
AIBullisharXiv – CS AI · Mar 96/10
🧠Researchers developed SecureRAG-RTL, a new AI framework that uses Retrieval-Augmented Generation to detect security vulnerabilities in hardware designs. The system improves detection accuracy by 30% on average across different LLM architectures and addresses the challenge of limited hardware security datasets for AI training.
AINeutralarXiv – CS AI · Mar 96/10
🧠Researchers introduce NGDBench, a comprehensive benchmark for evaluating neural networks' ability to work with graph databases across five domains including finance and medicine. The benchmark supports full Cypher query language capabilities and reveals significant limitations in current AI models when handling structured graph data, noise, and complex analytical tasks.
AIBullisharXiv – CS AI · Mar 66/10
🧠Researchers propose CTRL-RAG, a new reinforcement learning framework that improves large language models' ability to generate accurate, context-faithful responses in Retrieval-Augmented Generation systems. The method uses a Contrastive Likelihood Reward mechanism that optimizes the difference between responses with and without supporting evidence, addressing issues of hallucination and model collapse in existing RAG systems.
AIBullisharXiv – CS AI · Mar 37/107
🧠Researchers have developed Semantic XPath, a tree-structured memory system for conversational AI that improves performance by 176.7% over traditional methods while using only 9.1% of the tokens. The system addresses scalability issues in long-term AI conversations by efficiently accessing and updating structured memory instead of appending growing conversation history.
AIBullisharXiv – CS AI · Mar 36/106
🧠Researchers have developed S5-HES Agent, an AI-driven framework that democratizes smart home research by enabling natural language configuration of simulations without programming expertise. The system uses large language models and retrieval-augmented generation to make smart home environment testing accessible to broader research communities beyond traditional technical experts.
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AIBullisharXiv – CS AI · Mar 36/109
🧠Researchers introduce GAM-RAG, a training-free framework that improves Retrieval-Augmented Generation by building adaptive memory from past queries instead of relying on static indices. The system uses uncertainty-aware updates inspired by cognitive neuroscience to balance stability and adaptability, achieving 3.95% better performance while reducing inference costs by 61%.
AIBullisharXiv – CS AI · Mar 36/107
🧠NovaLAD is a new CPU-optimized document extraction pipeline that uses dual YOLO models for converting unstructured documents into structured formats for AI applications. The system achieves 96.49% TEDS and 98.51% NID on benchmarks, outperforming existing commercial and open-source parsers while running efficiently on CPU without requiring GPU resources.
AIBullisharXiv – CS AI · Mar 36/106
🧠Researchers developed TARSE, a new AI system for clinical decision-making that retrieves relevant medical skills and experiences from curated libraries to improve reasoning accuracy. The system performs test-time adaptation to align language models with clinically valid logic, showing improvements over existing medical AI baselines in question-answering benchmarks.
AIBullisharXiv – CS AI · Mar 36/106
🧠Researchers introduce 3R, a new RAG-based framework that optimizes prompts for text-to-video generation models without requiring model retraining. The system uses three key strategies to improve video quality: RAG-based modifier extraction, diffusion-based preference optimization, and temporal frame interpolation for better consistency.
AINeutralarXiv – CS AI · Mar 36/103
🧠Research on production RAG systems reveals that retrieval fusion techniques like multi-query retrieval and reciprocal rank fusion increase raw document recall but fail to improve end-to-end performance due to re-ranking limits and context constraints. The study found fusion variants actually decreased accuracy from 0.51 to 0.48 while adding latency overhead without corresponding benefits.
AIBearisharXiv – CS AI · Mar 36/104
🧠A new research study analyzes how Large Language Models are impacting Wikipedia content and structure, finding approximately 1% influence in certain categories. The research warns of potential risks to AI benchmarks and natural language processing tasks if Wikipedia becomes contaminated by LLM-generated content.
AINeutralarXiv – CS AI · Mar 36/104
🧠Researchers have developed AQUA, the first watermarking framework designed to protect image copyright in Multimodal Retrieval-Augmented Generation (RAG) systems. The framework addresses a critical gap in protecting visual content within RAG-as-a-Service platforms by embedding semantic signals into synthetic images that survive the retrieval-to-generation process.
AINeutralarXiv – CS AI · Mar 27/1012
🧠Researchers have developed an agentic LLM framework using Retrieval-Augmented Generation to automate adverse media screening for anti-money laundering compliance in financial institutions. The system addresses high false-positive rates in traditional keyword-based approaches by implementing multi-step web searches and computing Adverse Media Index scores to distinguish between high-risk and low-risk individuals.
AIBullisharXiv – CS AI · Mar 26/1013
🧠Researchers found that simple keyword search within agentic AI frameworks can achieve over 90% of the performance of traditional RAG systems without requiring vector databases. This approach offers a more cost-effective and simpler alternative for AI applications requiring frequent knowledge base updates.
AIBullisharXiv – CS AI · Mar 26/1013
🧠Researchers developed a domain-partitioned hybrid RAG system with knowledge graphs specifically for Indian legal research, combining three specialized pipelines for Supreme Court cases, statutory texts, and penal codes. The system achieved a 70% pass rate on legal questions, nearly doubling the performance of traditional RAG-only approaches at 37.5%.
AIBullisharXiv – CS AI · Mar 26/1017
🧠Researchers have developed Higress-RAG, a new enterprise-grade framework that addresses key challenges in Retrieval-Augmented Generation systems including low retrieval precision, hallucination, and high latency. The system introduces innovations like 50ms semantic caching, hybrid retrieval methods, and corrective evaluation to optimize the entire RAG pipeline for production use.
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AIBullisharXiv – CS AI · Feb 276/108
🧠Researchers developed GYWI, a scientific idea generation system that combines author knowledge graphs with retrieval-augmented generation to help Large Language Models generate more controllable and traceable scientific ideas. The system significantly outperforms mainstream LLMs including GPT-4o, DeepSeek-V3, Qwen3-8B, and Gemini 2.5 in metrics like novelty, reliability, and relevance.
AIBullisharXiv – CS AI · Feb 276/102
🧠Researchers developed a Retrieval-Augmented Generation (RAG) assistant for anatomical pathology laboratories to replace outdated static documentation with dynamic, searchable protocol guidance. The system achieved strong performance using biomedical-specific embeddings and could transform healthcare laboratory workflows by providing technicians with accurate, context-grounded answers to protocol queries.
