Retrieval-Augmented Generation (RAG) has demonstrated significant potential in enhancing large language models (LLMs) by supplementing external knowledge. However, existing approaches focus primarily on retrieving isolated factual knowledge entities while neglecting the critical reasoning relationships. To address this limitation, Graph-Augmented Generation (GraphRAG) has emerged as an effective solution, which explicitly integrates structured knowledge graphs to support complex reasoning tasks. Although diverse graph construction methods have been explored, they typically rely on static, query-agnostic graphs constructed via fixed heuristics. We are thereby motivated to propose a query-centric retrieval framework that adaptively constructs a graph tailored to each query. However, it is challenging to accurately identify these latent relationships from queries to the corpus. Moreover, unifying multiple local-perspective connections into a globally coherent structured corpus introduces additional complexity. To this end, we introduce HyperRAG, a novel framework in the Hyperbolic space that captures both explicit entity-based links and implicit query-aware connections. Extensive experiments on three benchmark datasets demonstrate that HyperRAG consistently outperforms existing baselines.

Financial management is high-stakes, where small errors can propagate into reporting deviations and costly downstream decisions, yet real-world workflows remain labor-intensive and fragmented, and existing automation supports only isolated steps rather than complete workflows. Large language models (LLMs) show promise in automating financial workflows, but current benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow-level evaluation. To address these gaps, we present **FinMaster**, a benchmark for evaluating large language models on full financial management workflows spanning financial literacy, accounting, auditing, and consulting. **FinMaster** comprises three modules: *FinSim* generates synthetic datasets compliant with real-world accounting standards for diverse company types, enabling realistic evaluation without relying on proprietary financial records. *FinSuite* offers 183 tasks across core financial domains. *FinEval* provides a unified evaluation framework. Extensive experiments on state-of-the-art models including GPT-4o-mini, Claude-3.7-Sonnet, and DeepSeek-V3 reveal critical capability gaps in financial reasoning, with accuracy dropping from over 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning. This degradation reflects error propagation, where accuracy reaches 58% for single-metric calculations but decreases to 37% in multi-metric settings. **FinMaster** provides scalable and reproducible benchmarking for realistic end-to-end financial workflows, helping advance reliable deployment of LLMs in financial practice.

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graphs. Leveraging community detection and relation filtering techniques, GraphRAG systems demonstrate inherent resistance to traditional RAG attacks, such as text poisoning and prompt injection. However, in this paper, we find that the security of GraphRAG systems fundamentally relies on the topological integrity of the underlying graph, which can be undermined by implicitly corrupting the logical connections, without altering surface-level text semantics. To exploit this vulnerability, we propose LogicPoison, a novel attack framework that targets logical reasoning rather than injecting false contents. Specifically, LogicPoison employs a type-preserving entity swapping mechanism to perturb both global logic hubs for disrupting overall graph connectivity and query-specific reasoning bridges for severing essential multi-hop inference paths. This approach effectively reroutes valid reasoning into dead ends while maintaining surface-level textual plausibility. Comprehensive experiments across multiple benchmarks demonstrate that LogicPoison successfully bypasses GraphRAG’s defenses, significantly degrading performance and outperforming state-of-the-art baselines in both effectiveness and stealth. Our code is available at <https://github.com/Jord8061/logicPoison>.

Retrieval-Augmented Generation (RAG) has long been a promising paradigm for enhancing large language models (LLMs) with external knowledge. Traditional embedding-based methods for graph construction can capture semantic similarity but struggle to establish fine-grained, interpretable logical relationships. Recently, Graph-enhanced RAG (GraphRAG) has gained increasing popularity for its capability in modeling logical relationships. However, graph construction requires extensive token consumption for triple extraction and summarization, making it costly and slow. Accordingly, we propose MeshRAG, a novel framework for mining efficient structures via hashing to enhance RAG. We adopt an inductive paradigm in which global graph structure emerges from local hash collisions rather than explicit symbolic extraction. By replacing neural embedding search with lightweight and bitwise operations, MeshRAG automates a simple and rapid graph construction process. Furthermore, the hash collision mechanism provides transparent evidence for logical connections and retrieval decisions. Experimental results show that MeshRAG outperforms existing baselines, while its graph construction requires no GPU resources or token budget and can structure over ten thousand chunks in a few minutes.

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM’s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model’s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model’s parametric knowledge, which undermines the model’s internal knowledge structure and increases the risk of misinterpreting the context. To this end, this paper proposes FaithfulRAG, a novel framework that resolves knowledge conflicts by explicitly modeling discrepancies between the model’s parametric knowledge and retrieved context. Specifically, FaithfulRAG identifies conflicting knowledge at the fact level and designs a self-thinking process, allowing LLMs to reason about and integrate conflicting facts before generating responses. Extensive experiments demonstrate that our method outperforms state-of-the-art methods. The code is available at https://github.com/DeepLearnXMU/Faithful-RAG.

Natural language has been extensively used for modeling text-attributed graphs with LLMs. Natural language is used to describe the graph for LLMs to understand or serve as component of the graph, e.g., textual attributes for embedding generation. However, natural language is inherently redundant and unstructured, making it unsuitable for modeling high-order neighbors with LLMs. Specifically, (i) graph descriptions become verbose, overwhelming LLMs, and (ii) only relying on attribute embeddings limits LLM’s ability to capture the adequate graph structural information. These limitations make it difficult to model graphs both concisely and adequately using sole natural language with LLMs.Inspired by the observation that LLMs pre-trained on one language can achieve exceptional performance on another with minimal additional training, we propose Graph-Defined Language for Large Language Model (GDL4LLM). This novel framework enables LLMs to transfer their powerful language understanding capabilities to graph-structured data. GDL4LLM translates the graph into a graph language corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. This corpus represents the subgraph centered around target nodes concisely with only a few tokens during fine-tuning on downstream tasks. By treating the graph as a new language, GDL4LLM enables LLMs to model text-attributed graph adequately and concisely. Extensive experiments on five datasets demonstrate that GDL4LLM outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors.