4arXiv cs.AI (Artificial Intelligence)·
DualG-MRAG: Dual-tier graph architecture decouples macro-reasoning and micro-matching for multimodal RAG
A new arXiv preprint introduces DualG-MRAG, a framework for Multimodal Retrieval-Augmented Generation that separates global structural reasoning (Macro Graph) from fine-grained local evidence matching (Micro Graph) to address retrieval noise in multi-hop reasoning tasks. The system uses a GNN-based retriever with query-driven message passing and a dynamic programming decoding mechanism that extracts explicit reasoning paths from the GNN's forward pass rather than feeding isolated document chunks to the generative model. Experiments show improvements over baselines on evidence recall and complex QA accuracy. The work targets a known failure mode of graph-enhanced MM-RAG systems where fine-grained visual features cause graph explosion and noise.
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GLM-RAG: Graph Language Model retrievers outperform GNNs and vector search on out-of-domain multi-hop reasoning
A new arXiv preprint introduces GLM-RAG, a retrieval-augmented generation framework using graph language model (GLM)-based retrievers over knowledge graphs. The authors compare GLM-based, GNN-based, and vector-search retrievers across single- and multi-hop QA settings, finding that finetuned GLM retrievers generalize better to unseen domains and achieve state-of-the-art on two multi-hop benchmarks. GNN-based retrievers offer better graph coverage with efficient training, while vector search excels at single-hop tasks. The work provides a systematic comparison of retriever architectures for knowledge-graph RAG with practical implications for domain transfer.
UMG-RAG: Training-free hybrid retrieval with uncertainty-aware granularity fusion for long-document RAG
Researchers propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a training-free hybrid retrieval framework that addresses the tension between large and fine-grained retrieval chunks in RAG pipelines. The system converts dense and sparse retriever scores across multiple chunk granularities into evidence distributions, estimates reliability via entropy, and fuses candidates using query-specific confidence signals. A variant called UMGP-RAG uses fine-grained hits to locate evidence while returning broader parent chunks for coherence. Experiments on QA benchmarks show improved generation quality with no changes to the underlying retriever or generator.
HKVM-RAG: Hypergraph key-value separation improves multi-hop retrieval-augmented generation
A new arXiv preprint introduces HKVM-RAG, an evidence-organization layer for multi-hop RAG that uses weighted hyperedges as retrieval keys while retaining passage text as answer values. Under a fixed-substrate protocol controlling for tuple cache, reader, and evaluation budget, the hypergraph key-value approach improves over KG-PPR by +3.4 F1 on 2WikiMultiHopQA and +3.6 F1 on MuSiQue. A dense-aware controller combining frozen ColBERTv2 with HKVM features reaches 88.8, 65.1, and 85.8 F1 on three benchmarks, outperforming ColBERTv2 alone by 5–11 F1 points. The work positions hypergraph organization as a reusable evidence-control mechanism rather than a dense-retrieval replacement.
Comparative framework for RAG variants: when GraphRAG and Agentic RAG are actually needed
A new arXiv preprint introduces a systematic evaluation framework comparing nine standardized RAG scenarios across regular RAG, GraphRAG, Modular RAG, and Agentic RAG on semi-structured knowledge bases. The authors propose a novel context engineering method that reduces token usage by 19–53% for GraphRAG and Agentic RAG by addressing context/memory overflow. A key finding is a 'retrieval-generation gap' where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate the benefits of advanced retrieval. The work targets practitioners building production RAG systems and provides data-driven guidance on when to use each variant.
MemDreamer: Hierarchical graph memory and agentic retrieval for long video understanding
MemDreamer is a plug-and-play framework that decouples perception and reasoning for long-video understanding by incrementally building a three-tier Hierarchical Graph Memory capturing spatiotemporal and causal relations. During inference, a reasoning model uses an Observation-Reason-Action loop with agentic tool-augmented retrieval to navigate the memory graph, constraining the context window to 2% of full-context ingestion while achieving a 12.5-point absolute accuracy gain. The system reaches SOTA on four benchmarks, narrowing the gap with human experts to 3.7 points. The authors also report a strong linear correlation between logical reasoning performance and long-video understanding, proposing agentic capability scaling as a new paradigm for multimodal comprehension.
DynaKRAG: Learnable state-conditioned control policy for multi-hop RAG evidence acquisition
DynaKRAG is a new framework that formulates multi-hop retrieval-augmented generation as a state-conditioned control problem over atomic evidence operations (iterative retrieval, query reformulation, sufficiency judging, etc.), using a learned controller to select among valid operations at each step. Evaluated with Qwen2.5-7B-Instruct, it achieves F1 scores of 0.5998 on HotpotQA, 0.5340 on 2WikiMultiHopQA, and 0.3061 on MuSiQue, outperforming the strongest baselines on all three benchmarks. Ablations show that replacing the learned controller with a uniform policy costs 3.96–5.78 F1 points, and that additional retrieval is not uniformly beneficial.
GRADRAG: Cross-component prompt adaptation for coordinated multi-agent RAG pipelines
GRADRAG is a new framework that treats a multi-agent RAG pipeline as a computational graph and propagates structured evaluator feedback upstream to iteratively update retriever, graph constructor, and answerer prompts. Unlike prior work that optimizes components in isolation, GRADRAG coordinates improvements across the full pipeline via a Prompt Optimizer and an early-stopping Evaluator. Evaluated on SQUALITY and QMSUM benchmarks under both flat chunk-based and graph-based retrieval paradigms, GRADRAG achieves a 12–15 percentage point net preference margin over one-step refinement baselines in LLM-judged pairwise comparisons.
RAGU: Open-source GraphRAG engine with compact 7B extractor model outperforming 32B baseline
RAGU is a new open-source modular GraphRAG engine that separates knowledge graph extraction from consolidation, using two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection. The authors train Meno-Lite-0.1, a 7B model specialized for language skills, which outperforms Qwen2.5-32B on knowledge-graph construction by 12.5% relative harmonic mean while matching it on English GraphRAG tasks. On GraphRAG-Bench (Medical), RAGU achieves evidence recall up to 0.84 versus ≤0.76 for competing systems. The system is MIT-licensed, pip-installable, and runs on a single GPU.