Knowledge_Graph
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Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
2026-03-16 10:36:13Madhulatha Mandarapu, Sandeep KunkunuruAbstract
Biomedical knowledge is fragmented across siloed databases -- Reactome for pathways, STRING for protein interactions, Gene Ontology for functional annotations, this http URL for study registries, and dozens more. Researchers routinely download flat files from each source and write bespoke scripts to cross-reference them, a process that is slow, error-prone, and not reproducible. We present two open-source biomedical knowledge graphs -- Pathways KG (118,686 nodes, 834,785 edges from 5 sources) and Clinical Trials KG (7,774,446 nodes, 26,973,997 edges from 5 sources) -- built on Samyama, a high-performance graph database written in Rust. Our contributions are threefold. First, we describe a reproducible ETL pattern for constructing large-scale KGs from heterogeneous public data sources, with cross-source deduplication, batch Cypher loading, and portable snapshot export. Second, we demonstrate cross-KG federation: loading both snapshots into a single graph tenant enables property-based joins across datasets, answering questions like ``Which biological pathways are disrupted by drugs currently in Phase~3 trials for breast cancer?'' -- a query that neither KG can answer alone. Third, we introduce schema-driven MCP server generation: each KG automatically exposes typed tools for LLM agents via the Model Context Protocol, enabling natural-language access to graph queries without manual tool authoring. All data sources are open-license (CC~BY~4.0, CC0, OBO). Snapshots, ETL code, and MCP configurations are publicly available. The combined federated graph (7.89M nodes, 27.8M edges) loads in 76 seconds on commodity hardware (Mac Mini M4, 16GB RAM), and the signature cross-KG query -- ``which pathways are disrupted by drugs in Phase~3 breast cancer trials?'' -- returns validated results in 2.1 seconds.
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URL
https://arxiv.org/abs/2603.15080
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Knowledge Activation: AI Skills as the Institutional Knowledge Primitive for Agentic Software Development
2026-03-16 04:10:35Gal BakalAbstract
Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation. The bottleneck to effective agentic software development is not model capability but knowledge architecture. When any knowledge consumer - an autonomous AI agent, a newly onboarded engineer, or a senior developer - encounters an enterprise task without institutional context, the result is guesswork, correction cascades, and a disproportionate tax on senior engineers who must manually supply what others cannot infer. This paper introduces Knowledge Activation, a framework that specializes AI Skills - the open standard for agent-consumable knowledge - into structured, governance-aware Atomic Knowledge Units (AKUs) for institutional knowledge delivery. Rather than retrieving documents for interpretation, AKUs deliver action - ready specifications encoding what to do, which tools to use, what constraints to respect, and where to go next - so that agents act correctly and engineers receive institutionally grounded guidance without reconstructing organizational context from scratch. AKUs form a composable knowledge graph that agents traverse at runtime - compressing onboarding, reducing cross - team friction, and eliminating correction cascades. The paper formalizes the resource constraints that make this architecture necessary, specifies the AKU schema and deployment architecture, and grounds long - term maintenance in knowledge commons practice. Organizations that architect their institutional knowledge for the agentic era will outperform those that invest solely in model capability.
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URL
https://arxiv.org/abs/2603.14805
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D-MEM: Dopamine-Gated Agentic Memory via Reward Prediction Error Routing
2026-03-15 20:44:43Yuru Song, Qi XinAbstract
Autonomous LLM agents require structured long-term memory, yet current "append-and-evolve" systems like A-MEM face O(N^2) write-latency and excessive token costs. We introduce D-MEM (Dopamine-Gated Agentic Memory), a biologically inspired architecture that decouples short-term interaction from cognitive restructuring via a Fast/Slow routing system based on Reward Prediction Error (RPE). A lightweight Critic Router evaluates stimuli for Surprise and Utility. Routine, low-RPE inputs are bypassed or cached in an O(1) fast-access buffer. Conversely, high-RPE inputs, such as factual contradictions or preference shifts, trigger a "dopamine" signal, activating the O(N) memory evolution pipeline to reshape the agent's knowledge graph. To evaluate performance under realistic conditions, we introduce the LoCoMo-Noise benchmark, which injects controlled conversational noise into long-term sessions. Evaluations demonstrate that D-MEM reduces token consumption by over 80%, eliminates O(N^2) bottlenecks, and outperforms baselines in multi-hop reasoning and adversarial resilience. By selectively gating cognitive restructuring, D-MEM provides a scalable, cost-efficient foundation for lifelong agentic memory.
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URL
https://arxiv.org/abs/2603.14597
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JobMatchAI An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
2026-03-15 19:13:41Mayank Vyaas, Abhijit Chakrabroty, Vivek GuptaAbstract
Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores. We introduce JobMatchAI, a production-ready system integrating Transformer embeddings, skill knowledge graphs, and interpretable reranking. Our system optimizes utility across skill fit, experience, location, salary, and company preferences, providing factor-wise explanations through resume-driven search workflows. We release JobSearch-XS benchmark and a hybrid retrieval stack combining BM25, knowledge graph and semantic components to evaluate skill generalization. We assess system performance on JobSearch-XS across retrieval tasks, provide a demo video, a hosted website and installable package.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14558
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An Alternative Trajectory for Generative AI
2026-03-14 22:33:28Margarita Belova, Yuval Kansal, Yihao Liang, Jiaxin Xiao, Niraj K. JhaAbstract
The generative artificial intelligence (AI) ecosystem is undergoing rapid transformations that threaten its sustainability. As models transition from research prototypes to high-traffic products, the energetic burden has shifted from one-time training to recurring, unbounded inference. This is exacerbated by reasoning models that inflate compute costs by orders of magnitude per query. The prevailing pursuit of artificial general intelligence through scaling of monolithic models is colliding with hard physical constraints: grid failures, water consumption, and diminishing returns on data scaling. This trajectory yields models with impressive factual recall but struggles in domains requiring in-depth reasoning, possibly due to insufficient abstractions in training data. Current large language models (LLMs) exhibit genuine reasoning depth only in domains like mathematics and coding, where rigorous, pre-existing abstractions provide structural grounding. In other fields, the current approach fails to generalize well. We propose an alternative trajectory based on domain-specific superintelligence (DSS). We argue for first constructing explicit symbolic abstractions (knowledge graphs, ontologies, and formal logic) to underpin synthetic curricula enabling small language models to master domain-specific reasoning without the model collapse problem typical of LLM-based synthetic data methods. Rather than a single generalist giant model, we envision "societies of DSS models": dynamic ecosystems where orchestration agents route tasks to distinct DSS back-ends. This paradigm shift decouples capability from size, enabling intelligence to migrate from energy-intensive data centers to secure, on-device experts. By aligning algorithmic progress with physical constraints, DSS societies move generative AI from an environmental liability to a sustainable force for economic empowerment.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14147
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The Reasoning Bottleneck in Graph-RAG: Structured Prompting and Context Compression for Multi-Hop QA
2026-03-14 17:29:13Yasaman Zarinkia, Venkatesh Srinivasan, Alex ThomoAbstract
Graph-RAG systems achieve strong multi-hop question answering by indexing documents into knowledge graphs, but strong retrieval does not guarantee strong answers. Evaluating KET-RAG, a leading Graph-RAG system, on three multi-hop QA benchmarks (HotpotQA, MuSiQue, 2WikiMultiHopQA), we find that 77% to 91% of questions have the gold answer in the retrieved context, yet accuracy is only 35% to 78%, and 73% to 84% of errors are reasoning failures. We propose two augmentations: (i) SPARQL chain-of-thought prompting, which decomposes questions into triple-pattern queries aligned with the entity-relationship context, and (ii) graph-walk compression, which compresses the context by ~60% via knowledge-graph traversal with no LLM calls. SPARQL CoT improves accuracy by +2 to +14 pp; graph-walk compression adds +6 pp on average when paired with structured prompting on smaller models. Surprisingly, we show that, with question-type routing, a fully augmented budget open-weight Llama-8B model matches or exceeds the unaugmented Llama-70B baseline on all three benchmarks at ~12x lower cost. A replication on LightRAG confirms that our augmentations transfer across Graph-RAG systems.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14045
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Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs
2026-03-14 16:16:47Hang Gao, Dimitris N. MetaxasAbstract
GraphRAG is increasingly adopted for converting unstructured corpora into graph structures to enable multi-hop reasoning. However, standard graph algorithms rely heavily on static connectivity and explicit edges, often failing in real-world scenarios where knowledge graphs (KGs) are noisy, sparse, or incomplete. To address this limitation, we introduce INSES (Intelligent Navigation and Similarity Enhanced Search), a dynamic framework designed to reason beyond explicit edges. INSES couples LLM-guided navigation, which prunes noise and steers exploration, with embedding-based similarity expansion to recover hidden links and bridge semantic gaps. Recognizing the computational cost of graph reasoning, we complement INSES with a lightweight router that delegates simple queries to Naïve RAG and escalates complex cases to INSES, balancing efficiency with reasoning depth. INSES consistently outperforms SOTA RAG and GraphRAG baselines across multiple benchmarks. Notably, on the MINE benchmark, it demonstrates superior robustness across KGs constructed by varying methods (KGGEN, GraphRAG, OpenIE), improving accuracy by 5%, 10%, and 27%, respectively.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14006
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TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
2026-03-12 22:41:53Qianggang Ding, Haochen Shi, Luis Castej\'on Lozano, Miguel Conner, Juan Abia, Luis Gallego-Ledesma, Joshua Fellowes, Gerard Conangla Planes, Adam Elwood, Bang LiuAbstract
We present a Temporal Rule-Anchored Chain-of-Evidence (TRACE) on knowledge graphs for interpretable stock movement prediction that unifies symbolic relational priors, dynamic graph exploration, and LLM-guided decision making in a single end-to-end pipeline. The approach performs rule-guided multi-hop exploration restricted to admissible relation sequences, grounds candidate reasoning chains in contemporaneous news, and aggregates fully grounded evidence into auditable \texttt{UP}/\texttt{DOWN} verdicts with human-readable paths connecting text and structure. On an S\&P~500 benchmark, the method achieves 55.1\% accuracy, 55.7\% precision, 71.5\% recall, and 60.8\% F1, surpassing strong baselines and improving recall and F1 over the best graph baseline under identical evaluation. The gains stem from (i) rule-guided exploration that focuses search on economically meaningful motifs rather than arbitrary walks, and (ii) text-grounded consolidation that selectively aggregates high-confidence, fully grounded hypotheses instead of uniformly pooling weak signals. Together, these choices yield higher sensitivity without sacrificing selectivity, delivering predictive lift with faithful, auditably interpretable explanations.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12500
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Shattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMs
2026-03-12 21:19:54Xing Zi, Xinying Zhou, Jinghao Xiao, Catarina Moreira, Mukesh PrasadAbstract
While Large Language Models (LLMs) achieve expert-level performance on standard medical benchmarks through single-hop factual recall, they severely struggle with the complex, multi-hop diagnostic reasoning required in real-world clinical settings. A primary obstacle is "shortcut learning", where models exploit highly connected, generic hub nodes (e.g., "inflammation") in knowledge graphs to bypass authentic micro-pathological cascades. To address this, we introduce ShatterMed-QA, a bilingual benchmark of 10,558 multi-hop clinical questions designed to rigorously evaluate deep diagnostic reasoning. Our framework constructs a topology-regularized medical Knowledge Graph using a novel $k$-Shattering algorithm, which physically prunes generic hubs to explicitly sever logical shortcuts. We synthesize the evaluation vignettes by applying implicit bridge entity masking and topology-driven hard negative sampling, forcing models to navigate biologically plausible distractors without relying on superficial elimination. Comprehensive evaluations of 21 LLMs reveal massive performance degradation on our multi-hop tasks, particularly among domain-specific models. Crucially, restoring the masked evidence via Retrieval-Augmented Generation (RAG) triggers near-universal performance recovery, validating ShatterMed-QA's structural fidelity and proving its efficacy in diagnosing the fundamental reasoning deficits of current medical AI. Explore the dataset, interactive examples, and full leaderboards at our project website: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2603.12458
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Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously
2026-03-12 17:59:51Yiran Guan, Liang Yin, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Jian Luan, Yuliang Liu, Xiang BaiAbstract
Online Video Large Language Models (VideoLLMs) play a critical role in supporting responsive, real-time interaction. Existing methods focus on streaming perception, lacking a synchronized logical reasoning stream. However, directly applying test-time scaling methods incurs unacceptable response latency. To address this trade-off, we propose Video Streaming Thinking (VST), a novel paradigm for streaming video understanding. It supports a thinking while watching mechanism, which activates reasoning over incoming video clips during streaming. This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning latency over video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts the offline VideoLLM to causal streaming reasoning, and VST-RL, which provides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an automated training-data synthesis pipeline that uses video knowledge graphs to generate high-quality streaming QA pairs, with an entity-relation grounded streaming Chain-of-Thought to enforce multi-evidence reasoning and sustained attention to the video stream. Extensive evaluations show that VST-7B performs strongly on online benchmarks, e.g. 79.5% on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remains competitive on offline long-form or reasoning benchmarks. Compared with Video-R1, VST responds 15.7 times faster and achieves +5.4% improvement on VideoHolmes, demonstrating higher efficiency and strong generalization across diverse video understanding tasks. Code, data, and models will be released at this https URL.
Abstract (translated)
在线视频大型语言模型（VideoLLMs）在支持响应式、实时交互中扮演着关键角色。现有方法主要集中在流媒体感知上，缺乏同步的逻辑推理过程。然而，直接应用测试时间缩放方法会导致无法接受的响应延迟。为解决这一权衡问题，我们提出了视频流思考（VST），这是一种新的用于视频理解的流媒体范式。它支持边看边想机制，在流媒体期间激活对传入视频片段的推理。这种设计通过将大型语言模型的推理延迟分摊到视频播放中来提高及时理解和连贯的认知，并保持实时响应性。 此外，我们还引入了一个全面的后训练流水线，该流水线整合了VST-SFT和VST-RL。VST-SFT结构性地将离线VideoLLM适应为因果流推理，而VST-RL则通过在多回合视频互动环境中自我探索提供端到端改进。我们还设计了一个自动化的训练数据合成流水线，该流水线使用视频知识图来生成高质量的流媒体问答对，并采用基于实体关系的连贯思考链来强制执行多重证据推理和对视频流的持续关注。 广泛的评估表明，VST-7B在在线基准测试（如StreamingBench上的79.5%和OVO-Bench上的59.3%）中表现强劲。同时，在离线长格式或推理基准上，VST也保持了竞争力。与Video-R1相比，VST的响应速度快了15.7倍，并在VideoHolmes上实现了+5.4%的改进，展示了更高的效率和跨多样视频理解任务的强大泛化能力。 代码、数据和模型将在以下网址发布：[https URL]。
URL
https://arxiv.org/abs/2603.12262
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BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning
2026-03-12 17:54:42Xiaoyu Ma, Lianyu Hu, Wenbing Tang, Zixuan Hu, Zeqin Liao, Zhizhen Wu, Yang LiuAbstract
Embodied task planning requires agents to execute long-horizon, goal-directed actions in complex 3D environments, where success depends on both immediate perception and accumulated experience across tasks. However, most existing LLM-based planners are stateless and reactive, operating without persistent memory and therefore repeating errors and struggling with spatial or temporal dependencies. We propose BrainMem(Brain-Inspired Evolving Memory), a training-free hierarchical memory system that equips embodied agents with working, episodic, and semantic memory inspired by human cognition. BrainMem continuously transforms interaction histories into structured knowledge graphs and distilled symbolic guidelines, enabling planners to retrieve, reason over, and adapt behaviors from past experience without any model fine-tuning or additional training. This plug-and-play design integrates seamlessly with arbitrary multi-modal LLMs and greatly reduces reliance on task-specific prompt engineering. Extensive experiments on four representative benchmarks, including EB-ALFRED, EB-Navigation, EB-Manipulation, and EB-Habitat, demonstrate that BrainMem significantly enhances task success rates across diverse models and difficulty subsets, with the largest gains observed on long-horizon and spatially complex tasks. These results highlight evolving memory as a promising and scalable mechanism for generalizable embodied intelligence.
Abstract (translated)
URL
https://arxiv.org/abs/2604.16331
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Behavioral Intelligence Platforms: From Event Streams to Autonomous Insight via Probabilistic Journey Graphs, Behavioral Knowledge Extraction, and Grounded Language Generation
2026-03-12 16:47:43Arun Patra, Bhushan VadgaveAbstract
Contemporary product analytics systems require users to pose explicit queries, such as writing SQL, configuring dashboards, or constructing funnels, before insights can surface. This pull-based paradigm creates a bottleneck: it requires both domain knowledge and technical fluency, and assumes practitioners know in advance which questions to ask. We argue that behavioral analytics should move from passive systems that answer queries to active systems that continuously detect and explain behavioral phenomena. We present the Behavioral Intelligence Platform (BIP), a system architecture that transforms raw event streams into automatically generated insights. BIP consists of four layers. First, Normalization and State Derivation (NSD) standardizes events and maps them to a semantic state hierarchy. Second, a Behavioral Graph Engine (BGE) models user journeys as absorbing Markov chains and computes transition probabilities, removal effects, and path quality metrics. Third, a Behavioral Knowledge Graph (BKG) and Detector System convert graph outputs into grounded behavioral facts and identify behavioral phenomena. Finally, a Grounded Language Layer constrains large language model outputs to verified facts, producing reliable narrative insights. We formalize the Behavioral Intelligence Problem, introduce a taxonomy of detectors for autonomous insight generation, and propose an interestingness score to prioritize insights under limited attention.
Abstract (translated)
URL
https://arxiv.org/abs/2604.22762
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OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion
2026-03-12 11:30:41Fr\'ed\'eric Ieng, Soror Sahri, Mourad Ouzzani, Massinissa Hammaz, Salima Benbernou, Hanieh Khorashadizadeh, Sven Groppe, Farah BenamaraAbstract
Knowledge Graphs (KGs) are widely used to represent structured knowledge, yet their automatic construction, especially with Large Language Models (LLMs), often results in incomplete or noisy outputs. Knowledge Graph Completion (KGC) aims to infer and add missing triples, but most existing methods either rely on structural embeddings that overlook semantics or language models that ignore the graph's structure and depend on external sources. In this work, we present OMNIA, a two-stage approach that bridges structural and semantic reasoning for KGC. It first generates candidate triples by clustering semantically related entities and relations within the KG, then validates them through lightweight embedding filtering followed by LLM-based semantic validation. OMNIA performs on the internal KG, without external sources, and specifically targets implicit semantics that are most frequent in LLM-generated graphs. Extensive experiments on multiple datasets demonstrate that OMNIA significantly improves F1-score compared to traditional embedding-based models. These results highlight OMNIA's effectiveness and efficiency, as its clustering and filtering stages reduce both search space and validation cost while maintaining high-quality completion.
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URL
https://arxiv.org/abs/2603.11820
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Understanding Wikidata Qualifiers: An Analysis and Taxonomy
2026-03-12 10:15:56Gilles Falquet, Sahar AljalboutAbstract
This paper presents an in-depth analysis of Wikidata qualifiers, focusing on their semantics and actual usage, with the aim of developing a taxonomy that addresses the challenges of selecting appropriate qualifiers, querying the graph, and making logical inferences. The study evaluates qualifier importance based on frequency and diversity, using a modified Shannon entropy index to account for the "long tail" phenomenon. By analyzing a Wikidata dump, the top 300 qualifiers were selected and categorized into a refined taxonomy that includes contextual, epistemic/uncertainty, structural, and additional qualifiers. The taxonomy aims to guide contributors in creating and querying statements, improve qualifier recommendation systems, and enhance knowledge graph design methodologies. The results show that the taxonomy effectively covers the most important qualifiers and provides a structured approach to understanding and utilizing qualifiers in Wikidata.
Abstract (translated)
本文对维基数据的限定符进行了深入分析，重点关注其语义和实际应用，并旨在开发一种分类体系来应对选择适当限定符、查询图谱以及进行逻辑推理方面的挑战。研究基于频率和多样性的考量评估了限定符的重要性，并采用了一种经过修改的香农熵指标以适应“长尾”现象的影响。通过对维基数据的备份文件进行分析，选出了排名前300位的限定符，并将其分类为上下文、知识/不确定性、结构以及其他类型的限定符。该分类体系旨在指导贡献者创建和查询陈述语句，改善限定符推荐系统，并提升知识图谱的设计方法论。研究结果表明，这种分类体系能够有效地涵盖最重要的限定符，并提供一种有条理的方法来理解和使用维基数据中的限定符。
URL
https://arxiv.org/abs/2603.11767
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KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generation
2026-03-12 03:40:30Qizhi Chen, Chao Qi, Yihong Huang, Muquan Li, Rongzheng Wang, Dongyang Zhang, Ke Qin, Shuang LiangAbstract
Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LLM) this http URL,this reliance on external data introduces new attack this http URL can inject poisoned texts into databases to manipulate LLMs into producing harmful target responses for attacker-chosen this http URL research primarily focuses on attacking conventional RAG this http URL,such methods are ineffective against this http URL robustness derives from the KG abstraction of GraphRAG,which reorganizes injected text into a graph before retrieval,thereby enabling the LLM to reason based on the restructured context instead of raw poisoned this http URL expose latent security vulnerabilities in GraphRAG,we propose Knowledge Evolution Poison (KEPo),a novel poisoning attack method specifically designed for this http URL each target query,KEPo first generates a toxic event containing poisoned knowledge based on the target this http URL fabricating event backgrounds and forging knowledge evolution paths from original facts to the toxic event,it then poisons the KG and misleads the LLM into treating the poisoned knowledge as the final this http URL multi-target attack scenarios,KEPo further connects multiple attack corpora,enabling their poisoned knowledge to mutually reinforce while expanding the scale of poisoned communities,thereby amplifying attack this http URL results across multiple datasets demonstrate that KEPo achieves state-of-the-art attack success rates for both single-target and multi-target attacks,significantly outperforming previous methods.
Abstract (translated)
GraphRAG（基于图的检索增强生成）通过从外部数据库构建知识图谱来提升大型语言模型（LLM）在时效性和准确性方面的性能。然而，这种对外部数据的依赖也引入了新的攻击面——攻击者可以向数据库中注入中毒文本，使LLM生成特定目标的有害回应。尽管现有研究主要关注于针对传统检索增强生成（RAG）系统的攻击方法，但这些方法对GraphRAG无效，因为它的鲁棒性源于KG抽象化过程：在检索之前将注入的文本重组为图结构，从而使LLM基于重新组织后的上下文进行推理，而非直接处理原始中毒数据。 为了揭示GraphRAG潜在的安全漏洞，我们提出了一种专门针对该模型设计的新攻击方法——知识演进毒害（KEPo）。对于每个目标查询，KEPo首先根据特定目标生成含有中毒信息的事件，并构建虚假背景及从原有事实到有毒事件的知识演化路径。随后将这些知识引入KG中，误导LLM将其视为最终知识源。在多目标攻击场景下，KEPo进一步连接多个攻击数据库集合，使各自注入的知识相互强化并扩大受感染社区规模，从而放大整体攻击效果。 实验结果表明，在多种数据集上进行测试时，KEPo实现了针对单目标和多目标攻击的最佳成功率，并显著优于先前方法。
URL
https://arxiv.org/abs/2603.11501
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MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries
2026-03-11 18:38:44Riccardo Campi, Nicol\`o Oreste Pinciroli Vago, Mathyas Giudici, Marco Brambilla, Piero FraternaliAbstract
Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples, thereby degrading performance in downstream Question-Answering (QA) tasks, particularly for multi-hop QA, which requires composing answers from multiple entities, facts, or relations. We propose a domain-agnostic, KG-based QA framework that covers both the indexing and retrieval/inference phases. A new indexing approach called Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and subsequently integrates them with entity-level summaries, thus avoiding the need for explicit traversal of edges in the graph during the QA retrieval phase. Complementing this, we introduce Decompose-Resolve (DR), a retrieval mechanism that decomposes user queries into resolvable triples and grounds them in the KG via iterative reasoning. Together, MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data. Experiments show that on standard and domain specific benchmarks, MDER-DR achieves substantial improvements over standard RAG baselines (up to 66%), while maintaining cross-lingual robustness. Our code is available at this https URL.
Abstract (translated)
基于知识图谱（KG）的检索增强生成（RAG）面临的一个问题是，索引方法可能在将文本缩减为三元组时丢失重要的上下文细节，从而导致下游问答任务（QA）性能下降，特别是在需要从多个实体、事实或关系中构建答案的多跳问答中。为此，我们提出了一种领域无关的知识图谱基础问答框架，涵盖索引和检索/推理阶段。 我们的方法包括一种新的称为映射-消歧-丰富-简化（MDER）的索引方式，它生成由上下文衍生出的三元组描述，并随后将这些描述与实体级别的摘要集成在一起。这种方法避免了在问答检索阶段显式遍历图中的边的需求。此外，我们还引入了一种称为分解-解析（DR）的检索机制，该机制将用户查询分解为可解析的三元组，并通过迭代推理将其锚定到知识图谱中。 MDER和DR共同形成了一个基于大语言模型的问答流水线，能够应对稀疏、不完整以及复杂的关系数据。实验表明，在标准和领域特定基准上，MDER-DR相比传统的RAG基线方法取得了显著改进（最高可达66%），同时保持了跨语言鲁棒性。 我们的代码可在提供的链接中获得。
URL
https://arxiv.org/abs/2603.11223
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A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification
2026-03-11 15:35:55Yichi Zhu, Kan Ling, Xu Liu, Hengrun Zhang, Huiqun Yu, Guisheng FanAbstract
Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models (LLMs) to this zero-tolerance domain is untenable due to their inherent factual unreliability, lack of traceability, and weakness in complex reasoning. To address these challenges, we introduce PharmGraph-Auditor, a novel system designed for safe and evidence-grounded prescription auditing. The core of our system is a trustworthy Hybrid Pharmaceutical Knowledge Base (HPKB), implemented under the Virtual Knowledge Graph (VKG) paradigm. This architecture strategically unifies a relational component for set constraint satisfaction and a graph component for topological reasoning via a rigorous mapping layer. To construct this HPKB, we propose the Iterative Schema Refinement (ISR) algorithm, a framework that enables the co-evolution of both graph and relational schemas from medical texts. For auditing, we introduce the KB-grounded Chain of Verification (CoV), a new reasoning paradigm that transforms the LLM from an unreliable generator into a transparent reasoning engine. CoV decomposes the audit task into a sequence of verifiable queries against the HPKB, generating hybrid query plans to retrieve evidence from the most appropriate data store. Experimental results demonstrate robust knowledge extraction capabilities and show promises of using PharmGraph-Auditor to enable pharmacists to achieve safer and faster prescription verification.
Abstract (translated)
药物错误对患者安全构成重大威胁，使得药师核验（PV）成为一项关键但负担沉重的最后一道防线。直接将大型语言模型（LLMs）应用于这种零容忍领域是不可行的，因为这些模型本身具有事实上的不准确性、缺乏可追溯性以及在复杂推理方面的弱点。为了解决这些问题，我们引入了PharmGraph-Auditor系统，这是一个旨在进行安全且基于证据的处方审计的新系统。我们的系统的核心是一个值得信赖的混合药物知识库（HPKB），该库是在虚拟知识图（VKG）范式下实现的。这种架构通过严格的映射层策略性地将用于集合约束满足的关联部分与用于拓扑推理的图形部分结合在一起。 为了构建这个HPKB，我们提出了迭代模式精炼（ISR）算法，这是一种框架，能够使图表和关系模式从医学文本中共同进化。对于审计任务，我们引入了基于知识库验证链（CoV）的新推理范式，该范式将LLM从一个不可靠的生成器转变为透明的推理引擎。CoV将审计任务分解为一系列针对HPKB的可验证查询序列，通过生成混合查询计划来检索来自最适当数据存储的证据。 实验结果表明了强大的知识提取能力，并展示了使用PharmGraph-Auditor使药师能够实现更安全和更快处方核验的巨大潜力。
URL
https://arxiv.org/abs/2603.10891
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Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval
2026-03-11 12:12:51Andrea Volpini, Elie Raad, Beatrice Gamba, David RiccitelliAbstract
Retrieval-Augmented Generation (RAG) systems typically treat documents as flat text, ignoring the structured metadata and linked relationships that knowledge graphs provide. In this paper, we investigate whether structured linked data, specifically this http URL markup and dereferenceable entity pages served by a Linked Data Platform, can improve retrieval accuracy and answer quality in both standard and agentic RAG systems. We conduct a controlled experiment across four domains (editorial, legal, travel, e-commerce) using Vertex AI Vector Search 2.0 for retrieval and the Google Agent Development Kit (ADK) for agentic reasoning. Our experimental design tests seven conditions: three document representations (plain HTML, HTML with JSON-LD, and an enhanced agentic-optimized entity page) crossed with two retrieval modes (standard RAG and agentic RAG with multi-hop link traversal), plus an Enhanced+ condition that adds rich navigational affordances and entity interlinking. Our results reveal that while JSON-LD markup alone provides only modest improvements, our enhanced entity page format, incorporating this http URL-style agent instructions, breadcrumbs, and neural search capabilities, achieves substantial gains: +29.6% accuracy improvement for standard RAG and +29.8% for the full agentic pipeline. The Enhanced+ variant, with richer navigational affordances, achieves the highest absolute scores (accuracy: 4.85/5, completeness: 4.55/5), though the incremental gain over the base enhanced format is not statistically significant. We release our dataset, evaluation framework, and enhanced entity page templates to support reproducibility.
Abstract (translated)
检索增强生成（RAG）系统通常将文档视为扁平文本，忽略了知识图提供的结构化元数据和链接关系。在这篇论文中，我们探讨了使用结构化的链接数据，特别是HTTP URL标记以及由链接数据平台提供的可解析实体页面，是否能提高标准和代理式RAG系统的检索准确性和答案质量。我们在四个领域（编辑、法律、旅行、电子商务）进行了一项受控实验，使用Vertex AI向量搜索2.0进行检索，并采用Google代理开发工具包（ADK）进行代理推理。 我们的实验设计测试了七种条件：三种文档表示形式（纯HTML、带有JSON-LD的HTML和增强优化的实体页面）、两种检索模式（标准RAG和多跳链接遍历的代理式RAG），再加上一种Enhanced+条件，该条件添加了丰富的导航功能和实体互链。我们的结果显示，虽然单独使用JSON-LD标记仅提供了适度改进，但包含this http URL风格的代理指令、面包屑导航以及神经搜索能力的增强实体页面格式实现了显著提升：标准RAG准确率提高了29.6%，代理式RAG管道的完整流程提高到了29.8%。Enhanced+变体，具有更丰富的导航功能，在绝对得分上取得了最高分（准确性：4.85/5，完整性：4.55/5），尽管相对于基础增强格式的增量改进并未达到统计显著性。 我们发布了数据集、评估框架和增强实体页面模板以支持可重复性。
URL
https://arxiv.org/abs/2603.10700
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FinReflectKG -- HalluBench: GraphRAG Hallucination Benchmark for Financial Question Answering Systems
2026-03-11 04:37:53Mahesh Kumar, Bhaskarjit Sarmah, Stefano PasqualiAbstract
As organizations increasingly integrate AI-powered question-answering systems into financial information systems for compliance, risk assessment, and decision support, ensuring the factual accuracy of AI-generated outputs becomes a critical engineering challenge. Current Knowledge Graph (KG)-augmented QA systems lack systematic mechanisms to detect hallucinations - factually incorrect outputs that undermine reliability and user trust. We introduce FinBench-QA-Hallucination, a benchmark for evaluating hallucination detection methods in KG-augmented financial QA over SEC 10-K filings. The dataset contains 755 annotated examples from 300 pages, each labeled for groundedness using a conservative evidence-linkage protocol requiring support from both textual chunks and extracted relational triplets. We evaluate six detection approaches - LLM judges, fine-tuned classifiers, Natural Language Inference (NLI) models, span detectors, and embedding-based methods under two conditions: with and without KG triplets. Results show that LLM-based judges and embedding approaches achieve the highest performance (F1: 0.82-0.86) under clean conditions. However, most methods degrade significantly when noisy triplets are introduced, with Matthews Correlation Coefficient (MCC) dropping 44-84 percent, while embedding methods remain relatively robust with only 9 percent degradation. Statistical tests (Cochran's Q and McNemar) confirm significant performance differences (p < 0.001). Our findings highlight vulnerabilities in current KG-augmented systems and provide insights for building reliable financial information systems, where hallucinations can lead to regulatory violations and flawed decisions. The benchmark also offers a framework for integrating AI reliability evaluation into information system design across other high-stakes domains such as healthcare, legal, and government.
Abstract (translated)
随着组织越来越多地将人工智能驱动的问答系统集成到金融信息系统中，以用于合规、风险评估和决策支持，确保AI生成输出的事实准确性已成为一项关键工程挑战。当前的知识图谱增强型问答系统缺乏系统性机制来检测幻觉——即那些破坏可靠性和用户信任的事实性错误输出。我们引入了FinBench-QA-Hallucination，这是一个用于评估基于SEC 10-K申报文件的知识图谱增强型金融问答中幻觉检测方法的基准测试。该数据集包含来自300页文档的755个标注样本，每个样本均采用保守的证据链接协议进行依据性标注，要求同时得到文本片段和提取的关系三元组的支持。我们在有无知识图谱三元组的两种条件下，评估了六种检测方法：大语言模型评判器、微调分类器、自然语言推理模型、跨度检测器以及基于嵌入的方法。结果显示，在干净条件下，基于大语言模型的评判器和嵌入方法表现最佳（F1值：0.82-0.86）。然而，当引入噪声三元组时，大多数方法性能显著下降，马修斯相关系数下降44%至84%，而嵌入方法仅下降9%，表现出相对稳健性。统计检验（Cochran's Q检验和McNemar检验）证实了性能差异的显著性（p < 0.001）。我们的研究结果揭示了当前知识图谱增强系统中的漏洞，并为构建可靠的金融信息系统提供了见解——在该系统中，幻觉可能导致违规和错误决策。该基准测试还为将AI可靠性评估整合到其他高风险领域（如医疗保健、法律和政府）的信息系统设计中提供了一个框架。
URL
https://arxiv.org/abs/2603.20252
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DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering
2026-03-10 03:44:52Tong Wang, Chi Jin, Yongkang Chen, Huan Deng, Xiaohui Kuang, Gang ZhaoAbstract
Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer reliability, and single-agent architectures that struggle with complex reasoning scenarios involving semantic relationships and multi-hop logic. This paper introduces DataFactory, a multi-agent framework that addresses these limitations through specialized team coordination and automated knowledge transformation. The framework comprises a Data Leader employing the ReAct paradigm for reasoning orchestration, together with dedicated Database and Knowledge Graph teams, enabling the systematic decomposition of complex queries into structured and relational reasoning tasks. We formalize automated data-to-knowledge graph transformation via the mapping function T:D x S x R -> G, and implement natural language-based consultation that - unlike fixed workflow multi-agent systems - enables flexible inter-agent deliberation and adaptive planning to improve coordination robustness. We also apply context engineering strategies that integrate historical patterns and domain knowledge to reduce hallucinations and improve query accuracy. Across TabFact, WikiTableQuestions, and FeTaQA, using eight LLMs from five providers, results show consistent gains. Our approach improves accuracy by 20.2% (TabFact) and 23.9% (WikiTQ) over baselines, with significant effects (Cohen's d > 1). Team coordination also outperforms single-team variants (+5.5% TabFact, +14.4% WikiTQ, +17.1% FeTaQA ROUGE-2). The framework offers design guidelines for multi-agent collaboration and a practical platform for enterprise data analysis through integrated structured querying and graph-based knowledge representation.
Abstract (translated)
表格问答（TableQA）使用户能够以自然语言的形式与结构化的表格数据进行交互。然而，现有的大型语言模型（LLM）方法面临关键限制：上下文长度的约束限制了它们处理数据的能力；幻觉问题影响了答案的可靠性；以及单一代理架构在涉及语义关系和多步逻辑推理的复杂场景中表现不佳。本文介绍了DataFactory，这是一个通过专门团队协作和自动化知识转换来解决这些限制的多智能体框架。该框架包含一个使用ReAct范式的数据领导者用于推理协调，同时还包括专门的数据库和知识图谱小组，这使得将复杂的查询分解为结构化和关系推理任务成为可能。我们通过映射函数T:D x S x R -> G实现了自动化数据到知识图谱的转换，并实施了基于自然语言的咨询机制——不同于固定工作流多智能体系统，这种机制允许灵活的代理间讨论和适应性规划以增强协作稳健性。此外，我们应用上下文工程技术整合历史模式和领域知识，从而减少幻觉并提高查询准确性。 在TabFact、WikiTableQuestions和FeTaQA数据集上使用来自五家提供商的八种LLM进行实验显示了持续的改进：我们的方法比基线提高了20.2%（TabFact）和23.9%（WikiTQ），Cohen's d值大于1表明效果显著。团队协作也优于单个团队变体，分别在TabFact、WikiTableQuestions和FeTaQA ROUGE-2上提高5.5%、14.4%和17.1%。 该框架为多智能体合作提供了设计准则，并通过集成结构化查询和基于图的知识表示提供了一个实用的企业数据分析平台。
URL
https://arxiv.org/abs/2603.09152
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