HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
Modular RAG: Transforming RAG systems into LEGO-like reconfigurable frameworks
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
Mandol unifies memory storage and retrieval into an agglomerative semantic graph architecture with quantitative query mechanisms, reporting best accuracy on LoCoMo and LongMemEval plus 5.4x retrieval and 4.8x insertion speedups.
Evaluates 9 RAG scenarios across variants, proposes context engineering reducing token usage 19-53%, and identifies a retrieval-generation gap where more retrieval does not improve generation proportionally.
A multi-agent semantic rewriting system for RAG cuts targeted privacy leakage from 144 to 1 instances on LLaMA-3-8B while raising BLEU-1 to 0.122 over SAGE's 0.117, with offline preprocessing.
KnowPilot integrates knowledge retrieval and memory systems into generative agents to achieve better results on domain-specific tasks such as text generation.
RAGe is a modular evaluation framework that correlates retrieval and generation quality with hardware constraints to recommend optimal RAG components for specific datasets.
citing papers explorer
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HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
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Mandol: An Agglomerative Agent Memory System for Long-Term Conversations
Mandol unifies memory storage and retrieval into an agglomerative semantic graph architecture with quantitative query mechanisms, reporting best accuracy on LoCoMo and LongMemEval plus 5.4x retrieval and 4.8x insertion speedups.
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Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization
Evaluates 9 RAG scenarios across variants, proposes context engineering reducing token usage 19-53%, and identifies a retrieval-generation gap where more retrieval does not improve generation proportionally.
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Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
A multi-agent semantic rewriting system for RAG cuts targeted privacy leakage from 144 to 1 instances on LLaMA-3-8B while raising BLEU-1 to 0.122 over SAGE's 0.117, with offline preprocessing.
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KnowPilot: Your Knowledge-Driven Copilot for Domain Tasks
KnowPilot integrates knowledge retrieval and memory systems into generative agents to achieve better results on domain-specific tasks such as text generation.
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RAGe: A Retrieval-Augmented Generation Evaluation Framework
RAGe is a modular evaluation framework that correlates retrieval and generation quality with hardware constraints to recommend optimal RAG components for specific datasets.
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