Multimodal recommendation advocates integrating the multimodal features of items with historical user behaviors to enhance recommendation accuracy across various online media platforms. The majority of existing methods concentrate on leveraging cross-modal learning over multimodal features to augment node representations. However, these approaches are confronted with two key challenges: i) augmented representations offer limited information gain for interactive prediction in the collaborative view, and ii) semantic discrepancy between the collaborative view and modality-augmented features remains inadequately addressed. To overcome these obstacles, we present a new Multi-view Semantic Contrastive Alignment (MSCA) approach for multimodal recommendation, which models and aligns node representations from multiple views. Specifically, we introduce a multi-view semantic pattern encoder that learns basic embeddings from the collaborative view and independently captures augmented semantic patterns from the item-item structural view and intra-modal view. Furthermore, a semantic contrastive alignment task is designed to mitigate the semantic divergence between collaborative embeddings and augmented representations by maximizing the mutual consistency between them, thereby facilitating an effective integration of both. Comprehensive experiments on three benchmark datasets confirm that the proposed MSCA consistently excels over diverse state-of-the-art baselines.
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Acoustic eavesdropping is a privacy risk, but existing attacks rarely work in real outdoor situations where people make phone calls on the move. We present SuperEar, the first portable system that uses acoustic metamaterials to reliably capture conversations in these scenarios. We show that the threat is real as a practical prototype can be implemented to enhance faint signals, cover the full range of speech with a compact design, and reduce noise and distortion to produce clear audio. We show that SuperEar can be implemented from low-cost 3D-printed parts and off-the-shelf hardware. Experimental results show that SuperEar can recover phone call audio with a success rate of over 80% at distances of up to 4.6 m - more than twice the range of previous approaches. Our findings highlight a new class of privacy threats enabled by metamaterial technology that requires attention.
The rapid evolution of the Web of Things (WoT) has created new opportunities for connectivity and standardization across heterogeneous devices, enabling the development of increasingly complex systems. However, edge devices deployed in resource-constrained scenarios face significant challenges. These devices require lightweight and efficient models to achieve high accuracy while operating within strict memory constraints. Typical approaches to model generation, Neural Architecture Search (NAS), have proven effective in automating the search for optimal architectures. However, existing NAS methods suffer from two critical limitations: (1) they fail to incorporate quantization into the search space, which can result in overlooking larger models that might perform better after quantization; and (2) current model evaluation methods struggle to provide accurate assessments within a short time period. To address these challenges, we propose EdgeGen, a novel NAS framework that integrates multiple quantization methods into the search process, enabling the discovery of larger models that need quantization to satisfy the constraint. EdgeGen employs a multi-beam Monte Carlo Tree Search (MCTS) algorithm and a constraint validator to explore the expanded search space efficiently, searching vast original and quantized models. Furthermore, EdgeGen evaluates the model performance following a GNN-based performance predictor, which provides a rapid and precise prediction. Across multiple benchmarks, EdgeGen consistently outperforms state-of-the-art NAS methods. Code available at: https://doi.org/10.5281/zenodo.18323232.
Trace analysis is essential for understanding system behaviors, detecting anomalies, and diagnosing faults in complex microservice-based web applications. Existing trace analysis approaches face several challenges in industrial microservice-based systems, including high manual overhead, limited functionality, unfriendly interaction mechanisms, and difficulties in deployment and integration. The strong capabilities of large language models (LLMs) in natural language understanding, reasoning, and multi-task generalization provide new opportunities for a more intelligent and flexible trace analysis approach. However, the trace analysis capabilities of LLMs remain underexplored and underdeveloped. To bridge this gap, we conduct the first comprehensive evaluation on the trace analysis capabilities of LLMs. In particular, we construct the first instruction&response benchmark dataset for trace analysis, named TraceBench. It involves a wide range of trace analysis tasks, allowing us to systematically evaluate the capabilities of LLMs in this area. Experimental results show that LLMs have potential in handling trace analysis tasks, but there leaves room for improvement. To this end, we propose TraceLLM, an approach that significantly enhances the capabilities of LLMs via fine-tuning, outperforming the open-source LLMs by 34.77% on average in terms of accuracy, and outperforming the closed-source model by 21.66% in the best case. The generalization and robustness of TraceLLM are also confirmed in our experiments. To the best of our knowledge, TraceLLM is the first LLM which is specialized for handling various types of trace analysis tasks. This work provides a foundation for future research to further explore the trace analysis capabilities of LLMs.
Traffic prediction serves as a cornerstone for systems and network services such as the Web of Vehicles (WoV), online navigation, and smart city applications. Despite the proliferation of model architectures in recent years, existing approaches often suffer from highly customized structures and weak transferability, making it difficult to cope with increasing task heterogeneity and modeling complexity. To address these challenges, we propose ST-LEGO, a modular assembly framework driven by large language models (LLMs) that supports flexible structural composition and automated code generation. ST-LEGO employs a multi-agent collaborative system comprising a Prompt Agent, Assemble Agent, and Code Agent, which are responsible for understanding task requirements, dynamically assembling structural modules, and automatically generating executable PyTorch code. By introducing a standardized module library and an intermediate structural description language (DSL), the framework enables controllable generation, reusable composition, and cross-task generalization of model architectures. Empirical results on multiple real-world traffic datasets demonstrate that models generated by ST-LEGO achieve superior accuracy, structural diversity, and convergence compared to a wide range of manually designed baselines. These results highlight the unique potential and scalability of LLMs as structural architects for traffic prediction, offering a new paradigm for integrating language models into web-interactive intelligent transportation systems.
The widespread use of social-media graphs has provided a convenient channel for rumor propagation. Rapid localization of rumor sources is therefore crucial for mitigating diffusion and enabling punitive countermeasures. Source Localization (SL) aims to identify the origin nodes given partial infection observations. Although deep-learning-based SL approaches outperform traditional estimators, three fundamental limitations remain: (i) Model Complexity —existing methods enrich node embeddings with cascades of auxiliary features, yielding high-capacity but excessively complex representations, leading to an exponential increase in the number of model parameters; (ii) Annotation gap —to overcome the scarcity of real-world misinformation cascades, current pipelines repeatedly simulate diffusion from a fixed seed, eroding robustness on true, few-shot outbreaks; and (iii) Computational bottleneck —full-model retraining or recurrent cascade simulation is required for every new task, which disqualifies the solutions from real-time deployment. Inspired by the success of prompt learning in NLP and graph learning, we propose LAPS, a Lightweight privilege-Allocation Prompting framework for Source localization. LAPS first trims parameter explosion and data scarcity by pre-training a graph-level source region classifier on adaptive subgraphs with source-prior diffusion data. It then enables few-shot SL via a privilege-allocation prompt module that updates <1% of all the parameters, avoiding model retraining to facilitate efficiency. Extensive experiments on five real-world networks demonstrate the effectiveness and efficiency of our prompt-based framework on few-shot source localization task.
Multimodal desire understanding, a task closely related to both emotion and sentiment that aims to infer human intentions from visual and textual cues, is an emerging yet underexplored task in affective computing with applications in social media analysis. Existing methods for related tasks predominantly focus on mining verbal cues, often overlooking the effective utilization of non-verbal cues embedded in images. To bridge this gap, we propose a Symmetrical Bidirectional Multimodal Learning Framework for Desire, Emotion, and Sentiment Recognition (SyDES). The core of SyDES is to achieve bidirectional fine-grained modal alignment between text and image modalities. Specifically, we introduce a mixed-scaled image strategy that combines global context from low-resolution images with fine-grained local features via masked image modeling (MIM) on high-resolution sub-images, effectively capturing intention-related visual representations. Then, we devise symmetrical cross-modal decoders, including a text-guided image decoder and an image-guided text decoder, which enable mutual reconstruction and refinement between modalities, facilitating deep cross-modal interaction. Furthermore, a set of dedicated loss functions is designed to harmonize potential conflicts between the MIM and modal alignment objectives during optimization. Extensive evaluations on the MSED benchmark demonstrate the superiority of our approach, which establishes a new state-of-the-art performance with 1.1% F1-score improvement in desire understanding. Consistent gains in emotion and sentiment recognition further validate its generalization ability and the necessity of utilizing non-verbal cues. Our code is available at: https://github.com/especiallyW/SyDES.
Taxonomy completion is the task of integrating new concepts into an existing taxonomy by determining the appropriate hypernym--hyponym relations. Existing approaches often struggle with the inherent imbalance between leaf and non-leaf edges, which induces bias in representation learning. In this paper, we propose BLEND: Balanced and Leaf-Enhanced Dual Fine-Tuning for Taxonomy Completion, a novel framework designed to mitigate this inductive bias. Our method employs independent fine-tuning of two lightweight large language models (LLMs): one optimized with a leaf-focused objective and the other trained with a balanced focused strategy. To further enhance structural understanding, we apply contrastive learning over structure-encoded paths and introduce a combined loss function, enabling more robust representation of hierarchical relations. Extensive experiments on three real-world benchmark datasets demonstrate that BLEND achieves up to 9.32% improvement in recall or hit metrics compared to state-of-the-art approaches. Moreover, BLEND delivers efficient inference while outperforming the latest baseline COMI, highlighting its effectiveness for taxonomy completion tasks.
Multimodal knowledge graphs (MMKG) extend the representational capacity of conventional knowledge graphs by integrating multimodal contexts. However, despite increasing attention on core tasks—multimodal link prediction (MMLP), multimodal entity alignment (MMEA), and multimodal entity linking (MMEL)—most existing approaches remain confined to meticulously tailored, task-specific and graph-specific paradigms, limiting their practicality in dynamic environments where tasks co-exist and new knowledge graphs continually emerge. In this work, we propose the first unified multi-task inductive inference framework for \MMKG (MtaIMKG) that can handle multiple core tasks, with universality and transferability to any unseen MMKG, by tackling inherent heterogeneities: i) Feature Heterogeneity: Bridging the semantic gaps in feature representations across domains. ii) Structural Heterogeneity: Overcoming structural divergence in topology across graphs. iii) Task Heterogeneity: Reconciling conflicting objectives and inductive biases across tasks. To address the above challenges, we center on reconciling divergent representation preferences across tasks, delivering only task-pertinent information instead of engaging in indiscriminate aggregation. Specifically, we introduce a task-aware routing network that dynamically integrates and routes multimodal representations, assesses their contributions under task semantics, and produces adaptive, task-conditioned representations for any input graph. In addition, MtaIMKG employs a gated mixture-of-experts to suppress cross-modal noise and extract complementary information across tasks. These strategies tackle the notable heterogeneity challenges in multi-task inductive inference on MMKG, enabling MtaIMKG to effectively zero-shot generalize to any unseen graph. Extensive experiments demonstrate that MtaIMKG achieves SOTA performance in both multi-task inductive and transductive inference with competitive efficiency, confirming its value as a scalable and generalizable solution for MMKG reasoning. Our code is released at https://github.com/MMKGer/MtaIMKG/.
Wikipedia, a vast and continuously consulted knowledge base, faces significant challenges in maintaining up-to-date content due to its reliance on manual human editors. Inspired by the vision of continuous knowledge acquisition in NELL and fueled by advances in LLM-based agents, this paper introduces WINEL, an agentic framework for continuously updating Wikipedia articles. Our approach employs a multi-agent framework to aggregate online information, select new and important knowledge for a target entity in Wikipedia, and then generate precise edit suggestions for human review. Our fine-grained editing models, trained on Wikipedia's extensive history of human edits, enable incorporating updates in a manner consistent with human editing behavior. Our editor models outperform both open-source instruction-following baselines and closed-source LLMs (e.g., GPT-4o) in key-information coverage and editing efficiency. End-to-end evaluation on high-activity Wikipedia pages demonstrates WINELL's ability to identify timely factual updates. This opens up a promising research direction in LLM agents for automatically updating knowledge bases in a never-ending fashion.
As social media and the World Wide Web become hubs for information dissemination, effectively organizing and understanding the vast amounts of dynamically evolving Web content is crucial. Knowledge graphs (KGs) provide a powerful framework for structuring this information. However, the rapid emergence of new hot topics, user relationships, and events in social media renders traditional static knowledge graph embedding (KGE) models rapidly outdated. Continual Knowledge Graph Embedding (CKGE) aims to address this issue, but existing methods commonly suffer from catastrophic forgetting, whereby older, but still valuable, information is lost when learning new knowledge (such as new memes or trending events). This means the model cannot effectively learn the evolution of the data. We propose a novel CKGE framework, BAKE. Unlike existing methods, BAKE formulates CKGE as a sequential Bayesian inference problem and utilizes the Bayesian posterior update principle as a natural continual learning strategy. This principle is insensitive to data order and provides theoretical guarantees to preserve prior knowledge as much as possible. Specifically, we treat each batch of new data as a Bayesian update to the model's prior. By maintaining the posterior distribution, the model effectively preserves earlier knowledge even as it evolves over multiple snapshots. Furthermore, to constrain the evolution of knowledge across snapshots, we introduce a continual clustering method that maintains the compact cluster structure of entity embeddings through a regularization term, ensuring semantic consistency while allowing controlled adaptation to new knowledge. We conduct extensive experiments on multiple CKGE benchmarks, which demonstrate that BAKE achieves the top performance in the vast majority of cases compared to existing approaches.
Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods address isolated distribution shifts, they overlook the critical co-evolution of label spaces and distributions under limited supervision and persistent uncertainty. To address this, we formalize Generalized Incremental Learning under Concept Drift (GILCD), characterizing the joint evolution of distributions and label spaces in open-environment streaming contexts, and propose a novel framework called Calibrated Source-Free Adaptation (CSFA). First, CSFA introduces a training-free prototype calibration mechanism that dynamically fuses emerging prototypes with base representations, enabling stable new-class identification without optimization overhead. Second, we design a novel source-free adaptation algorithm, i.e., Reliable Surrogate Gap Sharpness-aware (RSGS) minimization. It integrates sharpness-aware perturbation loss optimization with surrogate gap minimization, while employing entropy-based uncertainty filtering to discard unreliable samples. This mechanism ensures robust distribution alignment and mitigates generalization degradation caused by uncertainties. Thus, CSFA establishes a unified framework for stable adaptation to evolving semantics and distributions in open-world streaming scenarios. Extensive experiments validate the superior performance and effectiveness of CSFA compared to SOTA approaches.
Anonymous proofs of account ownership (anonymous PAOs) enable a prover to convince a verifier that the prover owns a valid account at a server without revealing their identity. Importantly, no server-side changes are required - the server does not even learn that such a proof is taking place. This functionality is particularly valuable in sensitive applications such as whistleblowing, or in settings where infrastructural changes at the server are impractical. Anonymous PAOs were first introduced by Wang et al. (IEEE S&P 2019), who realized them in the email setting via secure channel injection (SCI), a technique that requires carefully engineered multi-party computation (MPC) protocols. In this work, we propose YouChoose, a new approach to anonymous PAO that avoids MPC entirely, relying instead on the verifier to selectively forward TLS records. Compared to SCI, YouChoose is simpler, faster, and more adaptable across services. We also provide the first formal security definition for anonymous PAOs, establishing a rigorous framework for analysis and future constructions. Finally, we present a prototype implementation of YouChoose, evaluate it in the context of email and other social media accounts, showing its better performance than SCI with minimal latency overhead in practice.
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.
In the literature, prior research on Security-oriented Video Understanding (SVU) has predominantly focused on detecting and locating the threats (e.g., shootings, robberies) in videos, while largely lacking the effective capability to generate and evaluate the threat causes. Motivated by these gaps, this paper introduces a new chat paradigm SVU task, i.e., In-depth Security-oriented Video Understanding (DeepSVU), which aims to not only identify and locate the threats but also attribute and evaluate the causes of threatening segments in detail. Furthermore, this paper reveals two key challenges in the proposed task: 1) how to effectively model the coarse-to-fine physical-world information (e.g., human behavior, object interactions and background context) to boost the DeepSVU task, and 2) how to adaptively trade off these factors. Addressing these challenges is crucial for improving VAD, especially for identifying, locating, and attributing anomalies. To tackle these challenges, this paper proposes a new Unified Physical-world Regularized MoE (UPRM) approach. Specifically, UPRM incorporates two key components: the Unified Physical-world Enhanced MoE (UPE) Block and the Physical-world Trade-off Regularizer (PTR), to address the above two challenges, respectively. Extensive experiments conduct on our DeepSVU instructions datasets (i.e., UCF-C instructions and CUVA instructions) demonstrate that UPRM outperforms several advanced Video-LLMs as well as non-LLM approaches. such information.These justify the importance of the coarse-to-fine physical-world information in the DeepSVU task and demonstrate the effectiveness of our UPRM in capturing such information.
WebAssembly (Wasm) enables execution of memory-unsafe languages like C/C++ in the browser, but its monolithic linear memory leaves modules vulnerable to high-impact attacks such as cross-site scripting. Existing defenses are unsuitable for commercial-off-the-shelf (COTS) binaries, requiring either source code, non-standard runtimes, or introduce high-overhead instrumentation. We present Bento, the first static binary rewriter that automatically hardens COTS Wasm modules. Bento uses whole-program pointer analysis to partition memory into logical regions (stack, heap, globals) and rewrites the binary to map each region to an isolated instance using the standard Wasm multi-memory feature. This creates a software-based MMU that provides architectural hardening without adding instructions or costly runtime checks. Our evaluation shows that Bento mitigates real-world exploits in prominent applications, including Libpng and PDFAlto, while preserving program semantics, as confirmed by differential testing. Since our architectural approach adds no new instructions, Bento only induces 3% runtime slowdown and a 1% startup overhead.
LLM-powered agents are evolving from passive recommenders into autonomous executors, leveraging tools via the Model Context Protocol (MCP) for web automation. However, this paradigm introduces a new vulnerability: tool poisoning attacks that manipulate the MCP context can corrupt an agent's reasoning. Existing methods focus on anomaly detection and lack autonomous correction mechanisms, hindering their real-world deployment. In this paper, we introduce MCPFixGen, to the best of our knowledge, the first framework to provide automated anomaly detection and correction. We tackle three fundamental challenges: (1) when to initiate anomaly inspection with minimal overhead, (2) what features effectively characterize abnormality, and (3) how to automatically execute remedial actions. MCPFixGen achieves this through a novel multi-checkpoint rollback mechanism for anomaly management. MCPFixGen proposes a multi-checkpoint rollback anomaly management mechanism. Strategically places checkpoints at high-risk operations based on the tools-calling risk distribution. Then it identifies unanticipated tool behaviors by leveraging the correlation between attention activation values and tool calls. Finally, it introduces an attention-mask-based backtracking and correction mechanism. By masking the root cause of the anomaly, the LLM is prompted to re-infer, facilitating efficient and low-cost anomaly resolution. Our evaluation on real-world datasets demonstrates that MCPFixGen reduces interruptions caused by anomalies to below 13%, decreases the success rate of tool poisoning attacks to less than 0.3%, and significantly improves the robustness, computational efficiency, and task completion rate of web intelligence applications compared to existing approaches.
Privacy-preserving data trading allows data owners to sell data to consumers through a data trading web platform, the data market, without disclosing sensitive information in raw data. It enables legitimate data transmission and aggregation, facilitating large-scale data-driven model training. However, existing differential privacy-based approaches struggle to inject precisely calibrated noise in a trustworthy manner without revealing raw data to a third party, thus making them fail in achieving strong fairness and controllable privacy simultaneously, especially when facing malicious external adversaries or a corrupted data market. To address these issues, we present Xemis, a decentralized privacy-preserving data trading scheme, which builds upon a new multi-party computation (MPC)-based batched distributed noise sharing protocol, b-DNS. b-DNS facilitates the generation of threshold secret shares of Gaussian samples with low overhead and Byzantine robustness. Leveraging our designed game-theoretic mechanism and batched bits expansion mechanism, b-DNS achieves at least 12.7× faster and saves at least 43.6× bandwidth compared to ODO (EUROCRYPT'06) and CSU19 (CCS'19). Building atop b-DNS, Xemis further enables a decentralized, Byzantine robust data market to perturb the shares of blinded data under a controllable, precise noise level, without revealing the raw data or the perturbed data to the market. Additionally, Xemis utilizes a distributed demo dataset sampling-based mechanism and a Byzantine fault tolerance consensus-based method to enable fair value assessment and payment-data delivery. Consequently, Xemis achieves controllable privacy and strong fairness with Web3 compatibility under malicious market nodes, while achieving at least 3.3× faster than ZLM+24 (TIFS'24) with a 64-node market. The utility of noise-perturbed data is evaluated through image classification tasks on CIFAR-10, and when adding noise with privacy budget \privacyBudget=1 to 63% of the training data, the model maintains an accuracy of 83%.
While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new vulnerabilities to knowledge corruption attacks. Adversaries exploit these vulnerabilities by poisoning documents provided by RAG system to manipulate LLM outputs. To counter this threat, we propose SecureCollaRAG, a Byzantine-tolerant collaborative RAG framework leveraging Multi-source Knowledge Validation Mechanism. Our approach enables agent system to securely verify document provenance through dynamic GNN-based credibility scoring, effectively preventing stealthy knowledge corruption attacks while preserving essential domain knowledge integrity. Through extensive evaluations and formal analysis, we demonstrate that SecureCollaRAG maintains robustness against attackers under non-IID data distributions. Content warning: This paper contains unfiltered content generated by LLMs that may contain malicious contents.
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.