TIGRAG constructs token co-occurrence graphs for scalable graph-augmented RAG and uses iterative entity-driven retrieval to improve multi-hop QA performance over dense and prior graph methods.
AttentionRAG: Attention-guided context pruning in retrieval-augmented generation
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RAGP models prompt compression as redundancy-aware pruning on a multiplex graph using Lévy walks, achieving 49.3 average on LongBench at 4x compression versus 48.8 for LongLLMLingua at 3x.
DualView fuses local cross-attention and global context aggregation via adaptive gating to rerank fixed candidate sets for multi-hop QA, reporting 99.4% Top-4 Recall on MuSiQue at 4 ms latency while beating larger cross-encoders.
Hybrid Document-Routed Retrieval (HDRR) filters financial filings with LLM document routing then scopes chunk retrieval, beating pure chunk and pure file routing on FinDER accuracy and efficiency.
Neural reranking in a hybrid RAG system raises high-quality answer rates from 33.5% to 49.0% on financial report questions.
citing papers explorer
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Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs
TIGRAG constructs token co-occurrence graphs for scalable graph-augmented RAG and uses iterative entity-driven retrieval to improve multi-hop QA performance over dense and prior graph methods.
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Mapping Text to Multiplex Graph: Prompt Compression as L\'evy Walk-Guided Graph Pruning
RAGP models prompt compression as redundancy-aware pruning on a multiplex graph using Lévy walks, achieving 49.3 average on LongBench at 4x compression versus 48.8 for LongLLMLingua at 3x.
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DualView: Adaptive Local-Global Fusion for Multi-Hop Document Reranking
DualView fuses local cross-attention and global context aggregation via adaptive gating to rerank fixed candidate sets for multi-hop QA, reporting 99.4% Top-4 Recall on MuSiQue at 4 ms latency while beating larger cross-encoders.
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Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off
Hybrid Document-Routed Retrieval (HDRR) filters financial filings with LLM document routing then scopes chunk retrieval, beating pure chunk and pure file routing on FinDER accuracy and efficiency.
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Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
Neural reranking in a hybrid RAG system raises high-quality answer rates from 33.5% to 49.0% on financial report questions.
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