18 articles tagged with #rag-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AIBullisharXiv โ CS AI ยท 2d ago7/10
๐ง Researchers introduce soul.py, an open-source architecture addressing catastrophic forgetting in AI agents by distributing identity across multiple memory systems rather than centralizing it. The framework implements persistent identity through separable components and a hybrid RAG+RLM retrieval system, drawing inspiration from how human memory survives neurological damage.
AIBearisharXiv โ CS AI ยท 2d ago7/10
๐ง Researchers have developed ADAM, a novel privacy attack that exploits vulnerabilities in Large Language Model agents' memory systems through adaptive querying, achieving up to 100% success rates in extracting sensitive information. The attack highlights critical security gaps in modern LLM-based systems that rely on memory modules and retrieval-augmented generation, underscoring the urgent need for privacy-preserving safeguards.
AIBullisharXiv โ CS AI ยท 2d ago7/10
๐ง Researchers introduce Disco-RAG, a discourse-aware framework that enhances Retrieval-Augmented Generation (RAG) systems by explicitly modeling discourse structures and rhetorical relationships between retrieved passages. The method achieves state-of-the-art results on question answering and summarization tasks without fine-tuning, demonstrating that structural understanding of text significantly improves LLM performance on knowledge-intensive tasks.
AIBullisharXiv โ CS AI ยท 3d ago7/10
๐ง Researchers introduce CSAttention, a training-free sparse attention method that accelerates LLM inference by 4.6x for long-context applications. The technique optimizes the offline-prefill/online-decode workflow by precomputing query-centric lookup tables, enabling faster token generation without sacrificing accuracy even at 95% sparsity levels.
AIBearisharXiv โ CS AI ยท Mar 277/10
๐ง Researchers have developed PIDP-Attack, a new cybersecurity threat that combines prompt injection with database poisoning to manipulate AI responses in Retrieval-Augmented Generation (RAG) systems. The attack method demonstrated 4-16% higher success rates than existing techniques across multiple benchmark datasets and eight different large language models.
AINeutralarXiv โ CS AI ยท Mar 117/10
๐ง Researchers have developed an open-source benchmark dataset to evaluate AI systems' compliance with the EU AI Act, specifically focusing on NLP and RAG systems. The dataset enables automated assessment of risk classification, article retrieval, and question-answering tasks, achieving 0.87 and 0.85 F1-scores for prohibited and high-risk scenarios.
AINeutralarXiv โ CS AI ยท 2d ago6/10
๐ง Researchers present OIDA, a framework that adds epistemic structure to organizational knowledge systems by tracking commitment strength, contradiction status, and gaps in understanding. The framework introduces a QUESTION primitive that surfaces organizational ignorance with increasing urgency, addressing a capability absent from current retrieval-augmented generation (RAG) systems.
AIBullisharXiv โ CS AI ยท 2d ago6/10
๐ง Researchers introduce MCERF, a multimodal retrieval framework that combines vision-language models with LLM reasoning to improve question-answering from engineering documents. The system achieves a 41.1% relative accuracy improvement over baseline RAG systems by handling complex multimodal content like tables, diagrams, and dense technical text through adaptive routing and hybrid retrieval strategies.
AIBullisharXiv โ CS AI ยท 2d ago6/10
๐ง Researchers propose ITEM, an iterative utility judgment framework that enhances retrieval-augmented generation (RAG) systems by aligning with philosophical principles of relevance. The framework improves how large language models prioritize and process information from retrieval results, demonstrating measurable improvements across multiple benchmarks in ranking, utility assessment, and answer generation.
AIBullisharXiv โ CS AI ยท 2d ago6/10
๐ง Researchers introduce HiPRAG, a training methodology that improves agentic RAG systems by using fine-grained process rewards to optimize search decisions. The approach reduces inefficient search behaviors while achieving 65-67% accuracy across QA benchmarks, demonstrating that optimizing reasoning processes yields better performance than outcome-only training.
๐ง Llama
AINeutralarXiv โ CS AI ยท 2d ago6/10
๐ง RAGen is a new framework for generating domain-specific training data to improve Retrieval-Augmented Generation (RAG) systems. The system creates question-answer-context triples using semantic chunking, concept extraction, and Bloom's Taxonomy principles, enabling faster adaptation of LLMs to specialized domains like scientific research and enterprise knowledge bases.
AINeutralarXiv โ CS AI ยท 3d ago6/10
๐ง A research paper proposes a fundamental shift in how retrieval systems are evaluated, moving from traditional relevance-based metrics toward utility-centric optimization for large language models. This framework argues that retrieval effectiveness should be measured by its contribution to LLM-generated answer quality rather than document ranking alone, reflecting the structural changes introduced by retrieval-augmented generation (RAG) systems.
AIBullisharXiv โ CS AI ยท 6d ago6/10
๐ง Researchers introduce MAT-Cell, a neuro-symbolic AI framework that combines large language models with biological constraints to improve single-cell annotation accuracy. The system uses multi-agent reasoning and verification processes to overcome limitations in both supervised learning and LLM-based approaches, demonstrating superior performance on cross-species benchmarks.
AIBearisharXiv โ CS AI ยท Apr 66/10
๐ง Researchers have discovered LogicPoison, a new attack method that exploits vulnerabilities in Graph-based Retrieval-Augmented Generation (GraphRAG) systems by corrupting logical connections in knowledge graphs without altering text semantics. The attack successfully bypasses GraphRAG's existing defenses by targeting the topological integrity of underlying graphs, significantly degrading AI system performance.
AIBullisharXiv โ CS AI ยท Mar 266/10
๐ง Researchers propose a new four-phase architecture to reduce AI hallucinations using domain-specific retrieval and verification systems. The framework achieved win rates up to 83.7% across multiple benchmarks, demonstrating significant improvements in factual accuracy for large language models.
AIBullisharXiv โ CS AI ยท Mar 176/10
๐ง Researchers have developed QA-Dragon, a new Query-Aware Dynamic RAG System that significantly improves knowledge-intensive Visual Question Answering by combining text and image retrieval strategies. The system achieved substantial performance improvements of 5-6% across different tasks in the Meta CRAG-MM Challenge at KDD Cup 2025.
AINeutralarXiv โ CS AI ยท Mar 116/10
๐ง Researchers developed Budget-Constrained Agentic Search (BCAS) to evaluate how search depth, retrieval strategies, and token budgets affect accuracy and cost in AI search systems. The study found that hybrid retrieval methods with lightweight re-ranking produce the largest gains, with accuracy improving up to a small cap of additional searches.
AIBullisharXiv โ CS AI ยท Mar 26/1010
๐ง Researchers developed the TREC 2025 DRAGUN Track to evaluate AI systems that help readers assess news trustworthiness through automated report generation. The initiative created reusable evaluation resources including human-assessed rubrics and an AutoJudge system that correlates well with human evaluations for RAG-based news analysis tools.
