Attribution graphs reveal that RAG failures arise from shallow fragmented evidence flow in LLMs, enabling topology-based detection and targeted interventions that reinforce question-guided routing.
Retrieval augmented generation and understanding in vision: A sur- vey and new outlook.arXiv preprint arXiv:2503.18016
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CausalEmbed uses auto-regressive generation with iterative margin loss to produce multi-vector embeddings that reduce visual token counts 30-155x while retaining competitive performance on VDR benchmarks.
EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
Anisotropic self-supervised vision representations degrade approximate nearest-neighbor retrieval performance while more isotropic ones with local purity improve it.
AeroRAG improves fine-grained aerial visual question answering by converting images to scene graphs and using retrieval-augmented generation to create compact LLM prompts.
citing papers explorer
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Why Retrieval-Augmented Generation Fails: A Graph Perspective
Attribution graphs reveal that RAG failures arise from shallow fragmented evidence flow in LLMs, enabling topology-based detection and targeted interventions that reinforce question-guided routing.
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CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding
CausalEmbed uses auto-regressive generation with iterative margin loss to produce multi-vector embeddings that reduce visual token counts 30-155x while retaining competitive performance on VDR benchmarks.
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EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records
EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
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Geometric Analysis of Self-Supervised Vision Representations for Semantic Image Retrieval
Anisotropic self-supervised vision representations degrade approximate nearest-neighbor retrieval performance while more isotropic ones with local purity improve it.
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AeroRAG: Structured Multimodal Retrieval-Augmented LLM for Fine-Grained Aerial Visual Reasoning
AeroRAG improves fine-grained aerial visual question answering by converting images to scene graphs and using retrieval-augmented generation to create compact LLM prompts.