arXiv AI
Jul 22
M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation
arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.
PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation
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arXiv AI
Jun 9
UnWeaving the knots of GraphRAG -- turns out VectorRAG is almost enough
arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.
arXiv AI
1d ago
Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document Search
Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document Search describes DocuSearch, an offline multi‑agent system designed for telecom network operations. The system combines semantic vector search, BM25 full‑text search, and knowledge‑graph neighbor expansion, merges the results via Reciprocal Rank Fusion, and reranks with a cross‑encoder before pruning with Maximal Marginal Relevance. A per‑chunk evaluation loop ensures only grounded answers are returned, achieving Precision@10 of 0.69, Recall@10 of 0.79, and an 89.6% grounding rate—improvements of 15, 16, and 18.4 percentage points over a dense‑only baseline.
arXiv AI
Aug 24
EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering
arXiv:2608.21252v1 Announce Type: cross Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationshi...
arXiv AI
Jul 28
HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
arXiv:2607. 22597v1 Announce Type: new Abstract: Multi-hop question answering requires systems to retrieve evidence from multiple documents and connect scattered facts into a coherent reasoning process.
arXiv AI
Jun 4
QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples
arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.
arXiv AI
Jun 30
Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs
arXiv:2606. 30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure.
arXiv Computation and Language
3d ago
Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering
Hi-Q is a new framework for multi‑hop question answering that refines queries hierarchically based on evidence retrieved from a corpus. At each node it tests whether the current query unit is supported by evidence; if not, the node is expanded using a dependency‑preserving binary operator and verified for semantic coverage. The resulting query tree grows according to corpus support signals, and Hi‑Q achieves state‑of‑the‑art performance on three multi‑hop QA benchmarks, outperforming both iterative retrieval and graph‑based baselines without constructing a corpus‑wide graph.
arXiv AI
Jun 3
Cost-Aware Query Routing in RAG: Empirical Analysis of Retrieval Depth Tradeoffs
arXiv:2606. 02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency.
arXiv AI
Aug 11
VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge
arXiv:2608. 07994v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex product documentation like telecommunications.
arXiv AI
Jun 16
MAGE-RAG: Multigranular Adaptive Graph Evidence for Agentic Multimodal RAG in Long-Document QA
arXiv:2606. 15906v1 Announce Type: cross Abstract: Long-document multimodal question answering requires a system to locate sparse evidence in long PDFs and integrate clues from text, tables, images, charts, and complex layouts.
arXiv Machine Learning
Aug 27
A Storage-Retrieval Gap in Parametric Knowledge Graph Memory
The paper investigates a parametric approach to knowledge graph memory by compiling each entity into a LoRA adapter, enabling zero‑cost query-time retrieval via weight injection. On the MetaQA dataset, these adapters encode context‑free factual knowledge, improving exact‑match scores by up to +0.243 over a base model and achieving an oracle gap of +0.283. However, the stored knowledge is not recoverable through similarity or embedding‑based methods, indicating that knowledge is stored locally and does not transfer across semantically neighboring entities.