Query-Aware Knowledge Retrieval via Hyperbolic Structuring

NaviRAG: Learning to Navigate Knowledge Graphs for Retrieval-Augmented Generation

NaviRAG is a novel framework that reformulates retrieval as a reinforcement learning-driven dynamic navigation problem on schema-less knowledge graphs (KGs), achieving state-of-the-art performance in multi-hop QA while maintaining robustness in single-hop QA.

NGM-RAG: Neural Graph Matching based Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) significantly enhances the ability of Large Language Models (LLMs) to provide accurate and contextually relevant answers by dynamically integrating external databases. However, traditional RAG methods are primarily constrained by their reliance on text-based retrieval strategies, which often struggle with complex questions requiring multi-hop reasoning. To address this limitation, we introduce Neural Graph Matching based Retrieval-Augmented Generation (NGM-RAG), a novel framework that leverages graph structures to effectively capture and utilize relational knowledge for improved retrieval and answer generation. NGM-RAG explicitly incorporates graph construction, graph matching, and answer generation into a unified process. Within this framework, we propose a neural graph matching approach that combines text-based matching with Graph Neural Networks (GNNs). By employing an adaptive weighting strategy, NGM-RAG efficiently integrates multiple matching methods to select the most relevant contextual node information for answer generation. Experimental results on multi-hop question answering and long-context summarization tasks demonstrate that our NGM-RAG model achieves superior performance compared to both traditional NaiveRAG methods and state-of-the-art graph-enhanced approaches such as GraphRAG and LightRAG.

EvoGraph-R1: Self-Evolving Multimodal Knowledge Hypergraphs for Agentic Retrieval

EvoGraph-R1 is introduced, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.

Corpus-Centric Learning for Zero-Shot Table Retrieval

Tabular data represents a major source of structured knowledge for open-domain question answering (QA) and enterprise data lakes, yet effective table retrieval remains challenging due to the structure–semantics gap imposed by tabular layouts. Heuristic table linearization often leads to semantic loss, particularly for implicit queries, while recent supervised retrieval models (e.g., Birdie, Contr) rely heavily on large-scale query logs and labeled QA pairs, limiting their applicability in Day-0 cold-start scenarios. We propose GeCo-TR (Generative Schema and Contrastive Table Retrieval), a zero-shot table retrieval framework that eliminates the need for supervised QA data by shifting from direct query-to-table learning to modeling the intrinsic structural semantics of the table corpus. GeCo-TR introduces UHMI, a unified hybrid representation that integrates table structure with linked knowledge graph entities, and employs a hybrid neural–symbolic retrieval mechanism that dynamically combines dense semantic retrieval, symbolic graph traversal, and sparse lexical matching. This design enables robust semantic generalization while enforcing explicit structural constraints, resulting in high-precision and high-recall retrieval for implicit queries in a zero-shot setting. Extensive experiments on public benchmarks demonstrate the effectiveness of GeCo-TR; notably, on Open-WikiTable, it achieves 97.5% Recall@5 in the zero-shot setting, ranking second among all evaluated methods despite requiring no query-level supervision.

STaR: a soft-labeling and triplet-aware retriever for efficient retrieval-augmented QA

DuoFlow-KG: a dual-modal evidence retrieval framework for high-density LLM-augmented KGQA.

Knowledge Graph Question Answering has increasingly adopted a retrieval-reasoning decoupling paradigm, where large language models synthesize answers based on retrieved evidence subgraphs. However, existing retrieval methods often fail to jointly optimize semantic relevance and structural dependencies, resulting in fragmented evidence or search space explosion in multi-hop reasoning. In this work, we propose DuoFlow-KG, a unified dual-modal evidence retrieval framework that constructs compact, high-density evidence subgraphs through integrated structure-semantic modeling. Specifically, we introduce a dual-directional knowledge anchoring strategy that enriches entity representations by incorporating both incoming and outgoing relational neighborhoods with explicit inverse relation injection. A dual-modal fusion module is designed to project semantic resonance and topological distribution into a unified high-dimensional embedding space, where a scalar diffusion mechanism generates structural fingerprints to discriminate textually similar facts based on spatial reachability. Furthermore, we employ a hierarchical weak-supervision scheme, where diversity-aware sampling guided by Maximal Marginal Relevance is used to reduce redundancy and retain reasoning-critical evidence. Extensive experiments on WebQuestionsSP and ComplexWebQuestions benchmarks demonstrate that DuoFlow-KG achieves strong overall performance and best F1, achieving F1 scores of 77.28% and 61.33% respectively. Ablation studies confirm the complementary contributions of semantic modeling, structural reasoning, and bidirectional anchoring, particularly in complex multi-hop scenarios.