Revealing the underlying causal mechanisms in the real world is crucial for scientific and technological progress. Despite notable advances in recent decades, the lack of high-quality data and the reliance of traditional causal discovery algorithms (TCDA) on the assumption of no latent confounders, as well as their tendency to overlook the precise semantics of latent variables, have long been major obstacles to the broader application of causal discovery. To address this issue, we propose a novel causal modeling framework, TLVD, which integrates the metadata-based reasoning capabilities of large language models (LLMs) with the data-driven modeling capabilities of TCDA for inferring latent variables and their semantics. Specifically, we first employ a data-driven approach to construct a causal graph that incorporates latent variables. Then, we employ multi-LLM collaboration for latent variable inference, modeling this process as a game with incomplete information and seeking its Bayesian Nash Equilibrium (BNE) to infer the possible specific latent variables. Finally, to validate the inferred latent variables across multiple real-world web-based data sources, we leverage LLMs for evidence exploration to ensure traceability. We comprehensively evaluate TLVD on three de-identified real patient datasets provided by a hospital and two benchmark datasets. Extensive experimental results confirm the effectiveness and reliability of TLVD, with average improvements of 32.67% in Acc, 62.21% in CAcc, and 26.72% in ECit across the five datasets.
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Multi-modal knowledge graphs (MMKGs) enrich traditional knowledge graphs by incorporating heterogeneous modalities such as textual descriptions and visual content, offering complementary semantic cues for knowledge reasoning. However, existing approaches often overlook the structural dependencies within each modality, apply static or coarse-grained fusion strategies, and insufficiently model relational semantics. We propose a Multi-Granularity Multi-Modal Knowledge Graph Representation Learning Method via Subgraph-aware Adaptive Fusion and Hierarchical Relation Modeling (SAFER ), which implement multi-modal knowledge representation through adaptive fusion of multi-granularity information such as multi-modal semantics, knowledge structures and relations. SAFER explicitly constructs modality-specific subgraphs and employs structure-aware graph attention networks to effectively capture intra-modal structural dependencies. We propose an adaptive multi-modal fusion mechanism, which aggregates modality-specific embeddings at the semantic level by dynamically assigning entity-specific modality weights. We design a two-stage multi-granularity knowledge relation modeling strategy, which utilizes a structure-aware multi-modal adaptive pre-fusion to preserve topological information and a relation-aware graph attention network (RGAT) post-fusion to encode relational semantics. Extensive experiments on several benchmark datasets demonstrate that the proposed SAFER significantly outperforms competitive baselines on link prediction and relation reasoning tasks.
Knowledge Graph Question Answering (KGQA) aims to answer natural language questions by reasoning over structured knowledge graphs (KGs). While large language models (LLMs) have advanced KGQA through their strong reasoning capabilities, existing methods continue to struggle to fully exploit both the rich knowledge encoded in KGs and the reasoning capabilities of LLMs, particularly in complex scenarios. They often assume complete KG coverage and lack mechanisms to judge when external information is needed, and their reasoning remains locally myopic, failing to maintain coherent multi-step planning, leading to reasoning failures even when relevant knowledge exists. We propose Graph-RFT, a novel two-stage reinforcement fine-tuning KGQA framework with a ''plan–KGsearch–and–Websearch–during–think'' paradigm, that enables LLMs to perform autonomous planning and adaptive retrieval scheduling across KG and web sources under incomplete knowledge conditions. Graph-RFT introduces a chain-of-thought (CoT) fine-tuning method with a customized plan–retrieval dataset activates structured reasoning and resolves the GRPO cold-start problem. It then introduces a novel plan–retrieval guided reinforcement learning process integrates explicit planning and retrieval actions with a multi-reward design, enabling coverage-aware retrieval scheduling. It employs a Cartesian-inspired planning module to decompose complex questions into ordered sub-questions, and logical expression to guide tool invocation for globally consistent multi-step reasoning. This reasoning–retrieval process is optimized with a multi-reward combining outcome and retrieval-specific signals, enabling the model to learn when and how to combine KG and web retrieval effectively. Experiments on multiple KGQA benchmarks demonstrate that Graph-RFT achieves superior performance over strong baselines, even with smaller LLM backbones, and substantially improves complex question decomposition, factual coverage, and tool coordination.
Logical reasoning with large language models (LLMs) has received growing attention. One mainstream approach translates natural language into formal logic and then applies symbolic solvers for deduction. While effective in many tasks, these LLM-based translators often fail to generate consistent symbolic representations when the same concept appears in different linguistic forms. Such inconsistencies break logical coherence and lead to solver errors. However, most existing benchmarks lack this type of linguistic variation, which frequently occurs in real-world text, leaving the problem underexplored. To address this gap, we present SoLT, a benchmark that systematically rewrites reasoning datasets into diverse yet logically equivalent forms across multiple levels. Beyond evaluation, SoLT also provides a general method to enrich any dataset with linguistic diversity while preserving both meaning and logic. To further enhance the stability of LLM-based reasoning, we propose MenTaL, which explicitly guides models to build a concept–symbol mapping table during translation. By linking equivalent expressions to shared symbols, MenTaL maintains consistency and mitigates symbol drift. Experiments on SoLT demonstrate that LLMs indeed suffer from inconsistent symbol mapping under linguistic variation, leading to significant drops in reasoning accuracy. Meanwhile, applying MenTaL brings clear and stable performance improvements across diverse inputs. Overall, our findings reveal that overlooking linguistic diversity hides key weaknesses in LLM-based translators, and our work offers a step toward more reliable logical reasoning in varied real-world scenarios. Our code is available at https://github.com/wufeiwuwoshihua/LinguDiver.
Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, and to the best of our knowledge, no existing EA method considers aligning multiple (more than two) KGs. To fill this research gap, in this work, we study a novel problem of aligning multiple KGs and propose an effective framework named MultiEA to solve the problem. First, we embed the entities of all the candidate KGs into a common feature space by a shared KG encoder. Then, we explore three alignment strategies to minimize the distances among pre-aligned entities. In particular, we propose an innovative inference enhancement technique to improve the alignment performance by incorporating high-order similarities. Finally, to verify the effectiveness of MultiEA, we construct two new real-world benchmark datasets and conduct extensive experiments on them. The results show that our MultiEA can effectively and efficiently align multiple KGs in a single pass. We release the source code at: https://github.com/wzCSDN/MultiEA.
Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its discrete, ranking-based update mechanism. Unlike traditional FL methods that rely on model updates, FRL leverages discrete rankings as a communication parameter between clients and the server. This approach significantly reduces communication costs and limits an adversary's ability to scale or optimize malicious updates in the continuous space, thereby enhancing its robustness. This makes FRL particularly appealing for applications where system security and data privacy are crucial, such as web-based auction and bidding platforms. While FRL substantially reduces the attack surface, we demonstrate that it remains vulnerable to a new class of local model poisoning attack, i.e., fine-grained control attacks. We introduce the Edge Control Attack (ECA), the first fine-grained control attack tailored to ranking-based FL frameworks. Unlike conventional denial-of-service (DoS) attacks that cause conspicuous disruptions, ECA enables an adversary to precisely degrade a competitor's accuracy to any target level while maintaining a normal-looking convergence trajectory, thereby avoiding detection. ECA operates in two stages: (i) identifying and manipulating Ascending and Descending Edges to align the global model with the target model, and (ii) widening the selection boundary gap to stabilize the global model at the target accuracy. Extensive experiments across seven benchmark datasets and nine Byzantine-robust aggregation rules (AGRs) show that ECA achieves fine-grained accuracy control with an average error of only 0.224%, outperforming the baseline by up to 17×. Our findings highlight the need for stronger defenses against advanced poisoning attacks.
A vast amount of user-generated content is uploaded on social media everyday, potentially being collected as training data for large language models, and thus poses severe threats to individual privacy. Existing approaches for protecting user-generated content often fail to detect implicit privacy leaks which are not directly given but could be inferred from the contextual text (clues). The precise detection of leaks and clues is difficult but critical in text sanitization. We propose a graph-based model for the user-generated content and build a privacy leakage detector PrivSniffer which integrates the inference capability of language models with the precise graph search, to capture the contextual privacy leakage (CPL). To evaluate the effectiveness of the framework, we create SynthLeak, a dataset of dialogues containing human-labeled implicit leaks, while featuring diversity and naturalness. Our experimental results on SynthLeak and several other benchmarks reveal PrivSniffer's superior capability in detecting implicit leaks and CPLs. Particularly in CPL detection, the state-of-the-art approach achieves only an F1 Score of 0.36, whereas PrivSniffer attains 0.59 on SynthLeak, showing great promise in real-world text sanitization.
Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation learning. Specifically, we design an adversarially trained encoder that learns an invariant structural representation against diverse perturbations and derives a statistically independent watermark carrier, ensuring both robustness and transparency of watermarks. Meanwhile, we devise a graph-aware invertible neural network to provide a lossless channel for watermark embedding and extraction, guaranteeing high detectability and transparency of watermarks. Additionally, we develop a structure-aware editor that resolves the issue of latent modifications into discrete graph edits, ensuring robustness against structural perturbations. Experiments on diverse benchmark datasets demonstrate the superior effectiveness of DRGW.
The widespread proliferation of online content has intensified concerns about clickbait, deceptive or exaggerated headlines designed to attract attention. While Large Language Models (LLMs) offer a promising avenue for addressing this issue, their effectiveness is often hindered by Sycophancy, a tendency to produce reasoning that matches users' beliefs over truthful ones, which deviates from instruction-following principles. Rather than treating sycophancy as a flaw to be eliminated, this work proposes a novel approach that initially harnesses this behavior to generate contrastive reasoning from opposing perspectives. Specifically, we design a Self-renewal Opposing-stance Reasoning Generation (SORG) framework that prompts LLMs to produce high-quality ''agree'' and ''disagree'' reasoning pairs for a given news title without requiring ground-truth labels. To utilize the generated reasoning, we develop a local Opposing Reasoning-based Clickbait Detection (ORCD) model that integrates three BERT encoders to represent the title and its associated reasoning. The model leverages contrastive learning, guided by soft labels derived from LLM-generated credibility scores, to enhance detection robustness. Experimental evaluations on three benchmark datasets demonstrate that our method consistently outperforms LLM prompting, fine-tuned smaller language models, and state-of-the-art clickbait detection baselines. Our code is available in https://github.com/126541/ORCD.
Deep neural networks (DNNs) have achieved remarkable success across various domains, but their reliance on large datasets often containing sensitive information raises significant privacy concerns. In response to stringent privacy regulations such as GDPR and CCPA, machine unlearning (MUL) has emerged as a critical technique to remove the influence of specific data samples from trained models. However, existing unlearning methods face two major challenges: privacy deficiencies in the remaining datasets and significant instability of the model after unlearning. To address these issues, we propose DeepUL, a deep unlearning algorithm that leverages model sparsity to enhance both privacy and stability. DeepUL follows a two-step process: first pruning and then unlearning. The pruning step not only sparsifies the model but also decouples the model parameters from the original training data, thereby improving privacy. The unlearning step employs gradient projection to eliminate dependencies on the data to be deleted while maintaining the model stability. Additionally, we introduce a hierarchical weight pruning strategy to achieve differential pruning across network layers, preventing layer collapse and ensuring robustness. Extensive experiments on benchmark datasets demonstrate that DeepUL outperforms existing methods in utility, unlearning efficacy, privacy guarantee, and stability, making it a practical and effective solution for machine unlearning.
Mixing services run on blockchain trading systems, enhancing the transaction privacy of blockchain users. Yet, in recent years, the mixing services provide fertile ground for concealing illicit fund flows. Therefore, security experts make great efforts to find an effective de-anonymization of mixing services. Unfortunately, current de-anonymization technologies are constrained by a fundamental issue, i.e., the lack of a comprehensive, extensive, and reliably labeled benchmark dataset. To address this problem, we propose a new method for acquiring mixing transaction data. We design and implement a method named TGweaver, which actively executes the complete mixing workflow within a simulated blockchain environment. Furthermore, to enhance the realism of the dataset, we introduce a ''behavioral fingerprint'' mapping strategy. Ultimately, the proposed dataset includes over 891K transactions, scaling existing benchmark sizes by 2 to 4 orders of magnitude. In experiments, we use the proposed data to systematically evaluate existing de-anonymization techniques. Experimental results reveal that the current mixing address linking methods, based on heuristic rules, lacks generalization capability in complex scenarios, exhibiting low precision. In contrast, the methods utilizing supervised learning demonstrate significant advantages.
Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucinate on knowledge-intensive tasks. Retrieval-augmented generation (RAG) mitigates this issue by grounding answers in external sources, e.g., knowledge graphs (KGs). However, existing KG-based RAG approaches rely on semantics-unaware path sampling and are weakly aligned with KG reasoning objectives, which limits further accuracy gains. They also feed retrieved paths directly into the reasoner without organizing them into answer-centered reasoning paths, hindering small LLMs' ability to leverage the retrieved knowledge. Furthermore, prior works predominantly rely on large LLMs (e.g., ChatGPT/GPT-4) or assume backbones above 7B parameters, leaving sub-7B models underexplored. We address this gap with RPO-RAG, the first KG-based RAG framework specifically designed for small LLMs, to the best of our knowledge. RPO-RAG introduces three key innovations: (1) a query-path semantic sampling strategy that provides informative supervisory signals; (2) a relation-aware preference optimization that aligns training with intermediate KG reasoning signals (e.g., relation); and (3) an answer-centered prompt design that organizes entities and reasoning paths in an interpretable format. Extensive experiments on two benchmark Knowledge Graph Question Answering (KGQA) datasets, WebQSP and CWQ, demonstrate that RPO-RAG effectively bridges the performance gap between small and large language models. On WebQSP, it improves F1 by up to 8.8%, reflecting enhanced answer precision, while on CWQ it achieves new state-of-the-art results among models under 8B parameters in both Hit and F1. Overall, RPO-RAG substantially improves the reasoning capability of small LLMs—even under 3B parameters—highlighting their potential for resource-efficient and practical on-device KGQA applications.
Graph-based Retrieval-Augmented Generation (RAG) typically operates on binary Knowledge Graphs (KGs). However, decomposing complex facts into binary triples often leads to semantic fragmentation and longer reasoning paths, increasing the risk of retrieval drift and computational overhead. In contrast, n-ary hypergraphs preserve high-order relational integrity, enabling shallower and more semantically cohesive inference. To exploit this topology, we propose HyperRAG, a framework tailored for n-ary hypergraphs featuring two complementary retrieval paradigms: (i) HyperRetriever learns structural-semantic reasoning over n-ary facts to construct query-conditioned relational chains. It enables accurate factual tracking, adaptive high-order traversal, and interpretable multi-hop reasoning under context constraints. (ii) HyperMemory leverages the LLM's parametric memory to guide beam search, dynamically scoring n-ary facts and entities for query-aware path expansion. Extensive evaluations on WikiTopics (11 closed-domain datasets) and three open-domain QA benchmarks (HotpotQA, MuSiQue, and 2WikiMultiHopQA) validate HyperRAG's effectiveness. HyperRetriever achieves the highest answer accuracy overall, with average gains of 2.95% in MRR and 1.23% in Hits@10 over the strongest baseline. Qualitative analysis further shows that HyperRetriever bridges reasoning gaps through adaptive and interpretable n-ary chain construction, benefiting both open and closed-domain QA. Our codes are publicly available at https://github.com/Vincent-Lien/HyperRAG.git.
SketchMind: Understanding Abstract Sketches with MLLMs for Fine-Grained Sketch-Based Image Retrieval
Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) aims to retrieve images that accurately correspond to abstract hand-drawn sketches, requiring the model to understand sparse and abstract visual cues. Existing methods tend to rely on convolutional networks or metric learning to align sketch and image features, often overlooking the inherent abstraction and semantic ambiguity present in sketches. This limitation results in an insufficient understanding of fine-grained visual details. To address this challenge, we propose SketchMind, a novel method that leverages Multi-modal Large Language Models (MLLMs) to enhance abstract sketch understanding in FG-SBIR. Specifically, we use MLLMs to generate auxiliary textual descriptions based on the given sketches via a Visual Question Answering (VQA) strategy. To effectively incorporate these descriptions, we construct a graph structure with the sketch as the central node and the generated texts as peripheral nodes. A graph attention scheme is employed to perform uncertainty-aware feature fusion, enabling the model to suppress noisy or irrelevant textual information. Furthermore, to enhance both inter- and intra-modal fine-grained alignment, we design a Multi-scale Cross-modal Jigsaw Matching module in combination with a self-supervised learning strategy, which captures local and global visual correspondences across modalities more effectively. Extensive experiments on three benchmark FG-SBIR datasets demonstrate that SketchMind achieves superior performance over existing state-of-the-art methods, proving its effectiveness. Code is available at https://github.com/li1changxing/MLLM_FG_SBIR/.
Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow the retrieval-augmented generation (RAG) paradigm: first retrieving subgraphs relevant to the query and then generating answers conditioned on the retrieved subgraphs. However, such two-phase pipelines often struggle to faithfully incorporate graph structure, since the generation process is ultimately constrained by the quality and completeness of the retrieved subgraph. Although many advanced retrievers have been proposed recently to mitigate this issue, they are usually tailored to the training graphs and generalize poorly to unseen graphs, which limits their practical applicability. In this work, we propose Reasoning by Exploration (RoE), a novel approach that unifies retrieval and generation by framing reasoning over graphs as a process of graph exploration. At each step, the LLM selects candidate nodes and edges to explore, gradually constructing reasoning paths and generating answers along the way. To enable effective exploration, RoE is trained in two stages: supervised fine-tuning (SFT) on gold reasoning paths, followed by reinforcement learning (RL) to enhance exploration effectiveness and generalization. Experiments on benchmark datasets demonstrate that RoE achieves substantial overall improvements over baselines, while also generalizing effectively to unseen graphs. The code can be found at https://github.com/haoyuhan1/RoE.
Cross-modal retrieval is a fundamental task in multimedia understanding, aimed at querying samples with similar semantics in one modality (e.g., text) using another modality (e.g., image). Existing methods merely focus on point-to-point comparisons between individual samples, while overlooking the widely present many-to-many structural relationships in real-world scenarios. However, the many-to-many relationships formed by multiple samples sharing similar semantics are crucial for effectively achieving semantic alignment and accurately constructing shared semantic representations. To address this, we propose a novel hypergraph-based cross-modal retrieval approach, which explicitly establishes many-to-many associations between multiple samples using a label-driven hypergraph construction mechanism, combined with differentiated hyperedge weighting. Additionally, to avoid the limitation of information interaction direction imposed by traditional unidirectional cross-attention mechanisms, we design a bidirectional cross-attention structure, with image and text as separate query sources, to achieve symmetric semantic enhancement between modalities. The resulting joint image-text representations are then mapped as hypergraph vertices, further enhancing the model's ability to align cross-modal semantics. Since constructing a global hypergraph on a large-scale sample set would incur high computational cost, we introduce global label co-occurrence frequency to supervise the batch-level hypergraph construction, enhancing the local graph's ability to capture global semantics. Experimental results show that our model outperforms existing state-of-the-art methods on three benchmark cross-modal retrieval datasets.
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated content heavily relies on the usefulness of the retrieved information and the capacity of LLMs' internal information processing mechanism to incorporate it in answer generation. It is generally assumed that the retrieved information is relevant to the question. However, the retrieved information may have a variable degree of relevance and usefulness, depending on the question and the document collection. It is important to take into account the relevance of the retrieved information in answer generation. In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. We aim to build a RAG model that is more robust to varying levels of noisy context. Three types of explicit evaluation information are considered: relevance score, ranking score, and QPP (query performance prediction) score. The experimental results on five benchmark datasets demonstrate the effectiveness and better robustness of OpenDecoder by outperforming various baseline methods. Importantly, this paradigm is flexible to be integrated with the post-training of LLMs for any purposes and incorporated with any type of external indicators.
Domain-adaptive hashing enhances discriminative hash representations by transferring knowledge from a label-rich source domain to a label-scarce target domain. It has attracted significant attention due to its ability to enable efficient cross-domain retrieval without requiring target domain labels. However, existing methods generally assume that source domain labels are completely accurate. In practice, labels obtained via web crawling or crowdsourcing often contain varying degrees of noise, which hampers semantic alignment and aggravates domain shift. To tackle these issues, we propose a novel method termed Evidential Learning and Progressive Alignment (ELPA) for domain-adaptive hashing. This method comprises two key modules: the Uncertainty-aware Noise Separation (UNS) and the Progressive Cross-domain Alignment (PCA). In the UNS, we exploit the belief and uncertainty masses obtained from the evidential learning model and utilize the posterior probabilities of a Gaussian Mixture Model to effectively distinguish clean samples from noisy ones. In PCA, we introduce a progressive partial optimal transport mechanism that prioritizes pseudo-label generation for well-aligned target samples, thereby gradually achieving class-level and global-level cross-domain alignment. Extensive experiments across multiple benchmark datasets with various noise ratios demonstrate that ELPA consistently surpasses existing state-of-the-art methods, exhibiting superior robustness and generalization capability.
The rapid growth of digital communication has increased the demand for sticker retrieval systems that can match expressive stickers to users' communicative needs. In practice, however, sticker retrieval encounters significant out-of-distribution (OOD) challenges arising from unseen queries and stickers, driven by the diversity of user expression habits and sticker visual representations. These OOD issues often lead to irrelevant or inappropriate retrieval results, undermining the user experience. Drawing on symbolic interactionism in cognition, we propose XAlign-SR, a method that enhances OOD robustness by aligning abstract expressive intent between queries and stickers across modalities. To support this study, we construct OOD benchmarks from sticker datasets that simulate realistic query–sticker scenarios. Experiments demonstrate that our approach significantly outperforms state-of-the-art baselines.
Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning and web-scale search. On today's Web, where users increasingly expect to locate not only relevant pages but also specific long videos or fine-grained clips, existing benchmarks fall short due to limited video duration, low-quality captions, and coarse annotation granularity. To address these limitations, we introduce LoVR, a benchmark specifically designed for long video-text retrieval. LoVR contains 467 long videos and over 40,804 fine-grained clips with high-quality captions. To overcome the issue of poor machine-generated annotations, we propose an efficient caption generation framework that integrates VLM automatic generation, caption quality scoring, and dynamic refinement. This pipeline improves annotation accuracy while maintaining scalability. Furthermore, we introduce a semantic fusion method to generate coherent full-video captions without losing important contextual information. Our benchmark introduces longer videos, more detailed captions, and a larger-scale dataset, presenting new challenges for video understanding and retrieval. Extensive experiments on various advanced models demonstrate that LoVR is a challenging benchmark, revealing the limitations of current approaches and providing valuable insights for future research. We release the code link at https://github.com/TechNomad-ds/LoVR-benchmark/.