#rag-systems News & Analysis

56 articles tagged with #rag-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

56 articles

AIBearisharXiv – CS AI · Apr 66/10

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LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation

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 – CS AI · Mar 266/10

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Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval

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 – CS AI · Mar 176/10

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QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering

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 – CS AI · Mar 116/10

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Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search

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 – CS AI · Mar 26/1010

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Resources for Automated Evaluation of Assistive RAG Systems that Help Readers with News Trustworthiness Assessment

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.

AINeutralHugging Face Blog · May 195/10

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Introducing the Ettin Reranker Family

The article announces the Ettin Reranker Family, a new model architecture designed to improve information retrieval and ranking tasks in AI systems. This development represents a meaningful advance in neural ranking technology that could enhance search quality and recommendation systems across various applications.

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