Paper Digest: WWW 2026 Papers & Highlights
The Web Conference (WWW) is one of the top internet conferences in the world. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
Note: WWW-2026 accepts more than 950 papers, this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 950 WWW-2026 papers in a separate page, which takes quite some time to load.
To search for papers presented at WWW-2026 on a specific topic, please make use of the search by venue (WWW-2026) service. To summarize the latest research published at WWW-2026 on a specific topic, you can utilize the review by venue (WWW-2026) service. If you are interested in browsing papers by author, we have a comprehensive list of ~ 4,500 authors (WWW-2026). Additionally, you may want to explore our “Best Paper” Digest (WWW), which lists the most influential WWW papers since 2001.
Since 2018, Paper Digest has built a foundation of data spanning decades of conferences, journals, and research topics. The platform features a daily digest service that sifts through tens of thousands of new papers, clinical trials, news articles, and community posts, filtering the noise to highlight what matters most to specific interests. Beyond daily updates, dozens of built-in research tools streamline the academic workflow, supporting efficient reading and writing, comprehensive literature reviews, and automated research report generation.
Paper Digest Team
New York City, New York, 10017
team@paperdigest.org
TABLE 1: Paper Digest: WWW 2026 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | AgentPRM: Process Reward Models for LLM Agents Via Step-Wise Promise and Progress Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Instead, they should be evaluated based on their proximity to the goal and the progress they have made. Building on this insight, we propose a re-defined PRM for agent tasks, named AgentPRM, to capture both the interdependence between sequential decisions and their contribution to the final goal. |
Zhiheng Xi; Chenyang Liao; Guanyu Li; Zhihao Zhang; Wenxiang Chen; Binghai Wang; Senjie Jin; Yuhao Zhou; Jian Guan; Wei Wu; Tao Ji; Tao Gui; Qi Zhang; Xuanjing Huang; |
| 2 | Toward Generalized Web Agent Training: A Deep Dive Into Entropy-Balanced Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we delve into the challenges caused by entropy and propose the Agentic Entropy-Balanced Policy Optimization (AEPO), an agentic RL algorithm designed to balance entropy in both the rollout and policy update phases. |
Guanting Dong; Licheng Bao; Zhongyuan Wang; Kangzhi Zhao; Xiaoxi Li; Jiajie Jin; Jinghan Yang; Hangyu Mao; Fuzheng Zhang; Kun Gai; Guorui Zhou; Yutao Zhu; Ji-Rong Wen; Zhicheng Dou; |
| 3 | DeepAgent: A General Reasoning Agent with Scalable Toolsets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce DeepAgent, an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution within a single, coherent reasoning process. |
Xiaoxi Li; Wenxiang Jiao; Jiarui Jin; Guanting Dong; Jiajie Jin; Yinuo Wang; Hao Wang; Yutao Zhu; Ji-Rong Wen; Yuan Lu; Zhicheng Dou; |
| 4 | Can Multimodal LLMs Perform Time Series Anomaly Detection? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Built on the findings, we propose a MLLMs-based multi-agent framework TSAD-Agents to achieve automatic TSAD. |
Xiongxiao Xu; Haoran Wang; Yueqing Liang; Philip S. Yu; Yue Zhao; Kai Shu; |
| 5 | Online GPU Energy Optimization with Switching-Aware Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a practical online GPU energy optimization problem in a HPC scenarios. |
Xiongxiao Xu; Solomon Abera Bekele; Brice Videau; Kai Shu; |
| 6 | Self-Evolving LLMs Via Continual Instruction Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing CL approaches, such as replay-based and parameter isolation techniques, struggle with the catastrophic forgetting problem: new task training degrades performance on prior tasks due to the model’s adaptation to new data distributions, which weakens its generalization to old tasks. To address this issue, we propose a novel parameter-efficient adversarial MoE framework, MoE-CL, for industrial-scale self-evolving continual instruction tuning of LLMs. |
Jiazheng Kang; Le Huang; Cheng Hou; Zhe Zhao; ZhenXiang Yan; Ting Bai; |
| 7 | AC$2$L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework addressing both limitations through principled counterfactual reasoning. |
Kamal Berahmand; Saman Forouzandeh; Mehrnoush Mohammadi; Parham Moradi; Mahdi Jalili; |
| 8 | ScotRec: Social Chain-of-Thought LLM Reasoning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, LLMs are inherently prone to confirmation bias — the tendency to favor information that reinforces users’ existing views — which leads to an overemphasis on previously shown viewpoints and ignores diverse user beliefs for recommendations. To address this issue, in this paper, we propose SCoTRec, a social chain-of-thought reasoning framework for recommendation. |
Kaibei Li; Jie Zou; Qika Lin; Weikang Guo; Qinyang He; Yang Yang; |
| 9 | Doc-Researcher: A Unified System for Multimodal Document Parsing and Deep Research Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Processing such documents demands sophisticated parsing to preserve visual semantics, intelligent chunking to maintain structural coherence, and adaptive retrieval across modalities, which are capabilities absent in existing systems. In response, we present Doc-Researcher, a unified system that bridges this gap through three integrated components: (i) deep multimodal parsing that preserves layout structure and visual semantics while creating multi-granular representations from chunk to document level, (ii) systematic retrieval architecture supporting text-only, vision-only, and hybrid paradigms with dynamic granularity selection, and (iii) iterative multi-agent workflows that decompose complex queries, progressively accumulate evidence, and synthesize comprehensive answers across documents and modalities. |
Kuicai Dong; Shurui Huang; Fangda Ye; Wei Han; Zhi Zhang; Dexun Li; Wenjun Li; Qu Yang; Gang Wang; Yichao Wang; Chen Zhang; Yong Liu; |
| 10 | Adaptive Task Balancing for Visual Instruction Tuning Via Inter-Task Contribution and Intra-Task Difficulty Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, when learning multiple visual tasks simultaneously, this approach often results in suboptimal and imbalanced overall performance due to latent knowledge conflicts across tasks. To mitigate this issue, we propose a novel Adaptive Task Balancing approach tailored for visual instruction tuning (VisATB). |
Yanqi Dai; Yong Wang; Zebin You; Dong Jing; Xiangxiang Chu; Zhiwu Lu; |
| 11 | Generalized Incremental Learning Under Concept Drift Across Evolving Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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). |
En Yu; Jie Lu; Guangquan Zhang; |
| 12 | Read As You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose U-CoT+, a resource-efficient framework that prioritizes accessibility, flexibility and transparency in harmful meme detection by fully harnessing the capabilities of lightweight unimodal large language models (LLMs). |
Fengjun Pan; Xiaobao Wu; Tho Quan; Anh Tuan Luu; |
| 13 | RAFed: Responsive Augmentation and Approximate Update Method for Federated Learning with Non-IID Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To improve the adaptability of data augmentation policies to local data distributions, we introduce a Dynamic Adaptive Method for searching personalized augmentation policies tailored to heterogeneous clients. |
Yicheng Di; Zhanjie Zhang; |
| 14 | LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, inspired by human cognitive processes of mastering unseen tools through discovering and applying the logical information from prior experience, we introduce a novel Logic-Guided Semantic Bridging framework for inductive tool retrieval, namely, LoSemB, which aims to mine and transfer latent logical information for inductive tool retrieval without costly retraining. |
Luyao Zhuang; Qinggang Zhang; Huachi Zhou; Yujing Zhang; Xiao Huang; |
| 15 | Incentivizing Agentic Reasoning Capability with Outcome Supervision for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose AgenticKBQA, an LLM that can autonomously perform agentic reasoning on KBs to obtain answers. |
Zhuo Chen; Fei Wang; Zixuan Li; Zhao Zhang; Weiwei Ding; Chuanguang Yang; Yongjun Xu; Xiaolong Jin; |
| 16 | WiNELL: Wikipedia Never-Ending Updating with LLM Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Revanth Gangi Reddy; Tanay Dixit; Jiaxin Qin; Cheng Qian; Daniel Lee; Jiawei Han; Kevin Small; Xing Fan; Ruhi Sarikaya; Heng Ji; |
| 17 | Unleashing The Recommendation Power of Large Language Model Via Progressive Best Subset Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Progressive Best Subset Selection for LLM-based recommendation (LLMPBS), which targets test loss control and improves generalization through cluster-level selection. |
Yicheng Di; |
| 18 | GraphCogent: Mitigating LLMs’ Working Memory Constraints Via Multi-Agent Collaboration in Complex Graph Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, real-world graphs are often structurally complex, such as Web, Transportation, Social, and Citation networks. To address these limitations, we propose GraphCogent, a collaborative agent framework inspired by human Working Memory Model that decomposes graph reasoning into specialized cognitive processes: sense, buffer, and execute. |
Rongzheng Wang; Shuang Liang; Qizhi Chen; Yihong Huang; Muquan Li; Yizhuo Ma; Dongyang Zhang; Ke Qin; Man-Fai Leung; |
| 19 | Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet, zero-shot Moderators are biased toward neutral judgments, and no datasets exist for training them. To bridge this gap, we propose Debate-SFT, a post-training framework that leverages synthetic data to enhance agents’ ability to effectively adjudicate debates for claim verification. |
Haorui He; Yupeng Li; Dacheng Wen; Yang Chen; Reynold Cheng; Donglong Chen; Francis C. M. Lau; |
| 20 | DiffGRM: Diffusion-based Generative Recommendation Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, inter-digit heterogeneity: digits differ in semantic granularity and predictability, while the uniform next-token objective assigns equal weight to all digits, overtraining easy digits and undertraining hard digits. To address these two issues, we propose DiffGRM, a diffusion-based GR model that replaces the autoregressive decoder with a masked discrete diffusion model (MDM), thereby enabling bidirectional context and any-order parallel generation of SID digits for recommendation. |
Zhao Liu; Yichen Zhu; Yiqing Yang; Xiao Lv; Guoping Tang; Rui Huang; Qiang Luo; Ruiming Tang; Guorui Zhou; |
| 21 | PIGCN: Physics-Inspired Graph Convolution Networks for Heterogeneous Social Event Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, real-world diffusion is inherently multi-wave and oscillatory, where different messages interact through reinforcement and interference, resembling the principle of wave–particle duality. To address this gap, we propose Physics-Inspired Graph Convolution Networks (PIGCN ), a novel model that unifies wave-based propagation and particle-like interactions. |
Yongsheng Yu; Congbo Ma; Zitai Qiu; Shan Xue; Jian Yang; Jia Wu; |
| 22 | Question The Questions: Auditing Representation in Online Deliberative Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce an auditing framework for measuring the level of representation provided by a slate of questions, based on the social choice concept known as justified representation (JR). |
Soham De; Lodewijk Gelauff; Ashish Goel; Smitha Milli; Ariel D. Procaccia; Alice Siu; |
| 23 | GRank: Towards Target-Aware and Streamlined Industrial Retrieval with A Generate-Rank Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. … |
Yijia Sun; Shanshan Huang; Zhiyuan Guan; Qiang Luo; Ruiming Tang; Kun Gai; Guorui Zhou; |
| 24 | When Agents Trade: Live Multi-Market Trading Arena for LLM Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies test models instead of agents, cover limited periods and assets, and rely on unverified data. To address these gaps, we introduce Agent Market Arena (AMA), the first lifelong, real-time benchmark for evaluating LLM-based trading agents across multiple markets. |
Lingfei Qian; Xueqing Peng; Hanley Smith; Yi Han; Yueru He; Haohang Li; Yupeng Cao; Yangyang Yu; Guojun Xiong; Peng Lu; Yan Wang; Vincent Jim Zhang; Huan He; Alejandro Lopez-Lira; Jimin Huang; Jian-Yun Nie; Sophia Ananiadou; |
| 25 | Model Editing for New Document Integration in Generative Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, applying model editing to current GR models is not trivial, which is severely hindered by indistinguishable edit vectors across queries, due to the high overlap of shared docIDs in retrieval results. To address this, we propose DOME (docID-oriented model editing), a novel method that effectively and efficiently adapts GR models to unseen documents. |
Zhen Zhang; Zihan Wang; Xinyu Ma; Shuaiqiang Wang; Dawei Yin; Xin Xin; Pengjie Ren; Maarten de Rijke; Zhaochun Ren; |
| 26 | Causality Guided Representation Learning for Cross-Style Hate Speech Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by these observations, we hypothesize that the generation of hate speech can be modeled as a causal graph involving key factors: contextual environment, creator motivation, target, and style. Guided by this graph, we propose CADET, a causal representation learning framework that disentangles hate speech into interpretable latent factors and then controls confounders, thereby isolating genuine hate intent from superficial linguistic cues. |
Chengshuai Zhao; Shu Wan; Paras Sheth; Karan Patwa; K. Sel\c{c}uk Candan; Huan Liu; |
| 27 | Reasoning By Exploration: A Unified Approach to Retrieval and Generation Over Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Haoyu Han; Kai Guo; Harry Shomer; Yu Wang; Yucheng Chu; Hang Li; Li Ma; Jiliang Tang; |
| 28 | Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although numerous efforts have been devoted to building more effective search methods, existing approaches still show limitations in integrating contextual information, which hinders their ability to fully capture user intent. To address these challenges, we propose a context-aware reasoning-enhanced generative search framework for better understanding the complicated context. |
Zhiding Liu; Ben Chen; Mingyue Cheng; Enhong Chen; Li Li; Chenyi Lei; Wenwu Ou; Han Li; Kun Gai; |
| 29 | Financial Wind Tunnel: A Retrieval-Augmented Market Simulator Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Online financial systems for stock prediction, portfolio optimization, and algorithmic trading must remain robust against rare and volatile market events, but historical data often fails to capture diverse unprecedented financial risks, creating a major bottleneck for systematic stress testing. To address this, we propose Financial Wind Tunnel (FWT), a deployable, retrieval-augmented market simulator that generates realistic, controllable, and adaptable financial dynamics for industrial-scale training and testing. |
Bokai Cao; Xueyuan Lin; Yiyan Qi; Chengjin Xu; Cehao Yang; Jian Guo; |
| 30 | Bringing Reasoning to Generative Recommendation Through The Lens of Cascaded Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose CARE, a simple yet effective cascaded reasoning framework for debiased GR. |
Xinyu Lin; Pengyuan Liu; Wenjie Wang; Yicheng Hu; Chen Xu; Fuli Feng; Qifan Wang; Tat-Seng Chua; |
| 31 | Intelli-Planner: Towards Customized Urban Planning Via Large Language Model Empowered Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current planning methods heavily rely on human experts, which are time-consuming and labor-intensive, or utilize deep learning algorithms, often limiting stakeholder involvement. To bridge these gaps, we propose Intelli-Planner, a novel framework integrating Deep Reinforcement Learning (DRL) with large language models (LLMs) to facilitate participatory and customized planning scheme generation. |
Xixian Yong; Peilin Sun; Zihe Wang; Xiao Zhou; |
| 32 | OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. |
Fengran Mo; Zhan Su; Yuchen Hui; Jinghan Zhang; Jia Ao Sun; Zheyuan Liu; Chao Zhang; Tetsuya Sakai; Jian-Yun Nie; |
| 33 | Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing KG-based RAG approaches, however, generally assume that anchor entities are accessible to initiate graph traversal, which limits their robustness in open-world settings where accurate linking between the user query and the KG entity is unreliable. To overcome this limitation, we propose AnchorRAG, a novel multi-agent collaboration framework for open-world RAG without the predefined anchor entities. |
Jiasheng Xu; Mingda Li; Yongqiang Tang; Peijie Wang; Wensheng Zhang; |
| 34 | Pathways of Thoughts: Multi-Directional Thinking for Long-form Personalized Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Personalization is well studied in search and recommendation, but personalized question answering remains underexplored due to challenges in inferring preferences from long, noisy, implicit contexts and generating responses that are both accurate and aligned with user expectations. To address this, we propose Pathways of Thoughts (PoT), an inference-stage method that applies to any large language model (LLM) without task-specific fine-tuning. |
Alireza Salemi; Cheng Li; Mingyang Zhang; Qiaozhu Mei; Zhuowan Li; Spurthi Amba Hombaiah; Weize Kong; Tao Chen; Hamed Zamani; Michael Bendersky; |
| 35 | Generalized Pseudo-Relevance Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While recent large language models (LLMs)-based generative relevance feedback (GRF) enables model-free query reformulation, it either suffers from severe LLM hallucination or, again, relies on the relevance assumption to guarantee the effectiveness of rewriting quality. To overcome these limitations, we introduce an assumption-relaxed framework: Generalized Pseudo Relevance Feedback (GPRF), which performs model-free, natural language rewriting based on retrieved documents, not only eliminating the model assumption but also reducing dependence on the relevance assumption. |
Yiteng Tu; Weihang Su; Yujia Zhou; Yiqun Liu; Fen Lin; Qin Liu; Qingyao Ai; |
| 36 | ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. |
Haoxuan Li; He Chang; Yunshan Ma; Yi Bin; Yang Yang; See-Kiong Ng; Tat-Seng Chua; |
| 37 | SecureSplit: Mitigating Backdoor Attacks in Split Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, SL is susceptible to backdoor attacks, in which malicious clients subtly alter their embeddings to insert hidden triggers that compromise the final trained model. To address this vulnerability, we introduce SecureSplit, a defense mechanism tailored to SL. |
Zhihao Dou; Dongfei Cui; Weida Wang; Anjun Gao; Yueyang Quan; Mengyao Ma; Viet Vo; Guangdong Bai; Zhuqing Liu; Minghong Fang; |
| 38 | TGSBM: Transformer-Guided Stochastic Block Model for Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose TGSBM (Transformer-Guided Stochastic Block Model), a framework that integrates the principled generative structure of Overlapping Stochastic Block Models with the representational power of sparse Graph Transformers. |
Zhejian Yang; Songwei Zhao; Zilin Zhao; Hechang Chen; |
| 39 | Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To advance meta-cognitive editing, we propose MIND (Meta-cognitive INtegrated Dynamic Knowledge Editing), a framework that constructs a meta-knowledge memory for self-awareness, employs game-theoretic interactions to monitor knowledge activation, and incorporates label refinement for noise-robust updates. |
Zhaoyu Fan; Kaihang Pan; Mingze Zhou; Bosheng Qin; Juncheng Li; Shengyu Zhang; Wenqiao Zhang; Siliang Tang; Fei Wu; Yueting Zhuang; |
| 40 | Cost-Aware Retrieval-Augmentation Reasoning Models with Adaptive Retrieval Depth Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we make the following contributions: (1) we propose a retrieval-augmented reasoning model that dynamically adjusts the length of the retrieved document list based on the query and retrieval results; (2) we develop a cost-aware advantage function for training of efficient retrieval-augmented reasoning models through reinforcement learning; and (3) we explore both memory- and latency-bound implementations of the proposed cost-aware framework for both proximal and group relative policy optimization algorithms. |
Helia Hashemi; Victor R\{u}hle; Saravan Rajmohan; |
| 41 | A Frequency-Aware Mixture of Heterogeneous Experts Framework for Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our data-driven analysis reveals that existing encoders exhibit characteristic frequency biases (e.g., self-attention tends to emphasize low-frequency patterns), highlighting the limitations of any single architecture. To address this problem, we propose FA-KT, a frequency-aware mixture of heterogeneous experts framework. |
Youheng Bai; Mingliang Hou; Teng Guo; Zitao Liu; Weiqi Luo; |
| 42 | Breaking The Single-Reference-Vector Barrier in Approximate Nearest Neighbor Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To effectively and efficiently support all/any-k ANN search, we first propose distance metrics to evaluate the ranking of vectors among those in the dataset for exact all/any-k NN. Building on this, we introduce search algorithms and prove they can search according to the proposed distance metrics on graph indexes designed for traditional ANN. |
Jiadong Xie; Jeffrey Liang; Siyi Teng; Jeffrey Xu Yu; Yingfan Liu; |
| 43 | PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce PLUM, a framework designed to adapt pre-trained LLMs for industry-scale recommendation tasks. |
Ruining He; Lukasz Heldt; Lichan Hong; Raghunandan Keshavan; Shifan Mao; Nikhil Mehta; Zhengyang Su; Alicia Tsai; Yueqi Wang; Shao-Chuan Wang; Xinyang Yi; Lexi Baugher; Baykal Cakici; Ed Chi; Cristos Goodrow; Ningren Han; He Ma; Romer Rosales; Abby Van Soest; Devansh Tandon; Su-Lin Wu; Weilong Yang; Yilin Zheng; |
| 44 | Diffusion Generative Recommendation with Continuous Tokens Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by the emerging trend of embracing continuous tokens in language models, we propose ContRec, a novel framework that seamlessly integrates continuous tokens into LLM-based RecSys. |
Haohao Qu; Shanru Lin; Yujuan Ding; Yiqi Wang; Wenqi Fan; |
| 45 | Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Without recommendation-oriented design, they often underuse the collaborative signals in the user–item interaction history, leading to unsatisfying recommendation results. To address this, we propose the Multi-Agent Collaborative Filtering (MACF) framework for agentic recommendations, drawing an analogy between traditional collaborative filtering algorithms and LLM-based multi-agent collaboration. |
Yu Xia; Sungchul Kim; Tong Yu; Ryan A. Rossi; Julian McAuley; |
| 46 | Space-based Parameter Evolving with Lightweight Optimization for Graph Adaptation to Evolving Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose STEM (State-based Parameter Evolving with Lightweight Optimization), a replay-free framework that transforms continual adaptation into controlled parameter space evolution via a controller-worker architecture. |
Junyu Luo; Zixuan Ouyang; Xiao Luo; Hourun Li; Zhiping Xiao; Yifan Wang; Ming Zhang; |
| 47 | Unequal Vulnerability: The Differential Impact of Label Flipping Attacks Across Classes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We provide a rigorous theoretical analysis, demonstrating that a lower standardized separation between classes fundamentally leads to greater vulnerability. Grounded in this insight, we propose Confusability-Aware Contrastive Learning (CACL), a targeted defense that maximizes the feature-space separation for the most vulnerable class pairs. |
Pinlong Zhao; Mengyang Li; Pengfei Jiao; Huijun Tang; Ou Wu; |
| 48 | Smart Eye: LLM-Guided Proposer-Verifier Framework for Industrial-Scale Log Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Smart Eye, a deployable two-stage key pattern retrieval framework that employs large language models (LLMs) strictly as proposal engines, coupled with a deterministic verifier. |
Changhua Pei; Hang Cui; Jingjing Li; Yuxuan Li; Zihan Liu; Xinyuan Liao; Cenjie Hu; Jiabao Wang; Zheyuan Li; Zexin Wang; Haotian Si; Ke Xiang; Gaogang Xie; Dan Pei; |
| 49 | Difference-based Sample Selection for Federated Graph Rationalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Graph rationalization methods aim to improve the explainability of Graph Neural Networks by identifying critical subgraphs (rationales) for task prediction. |
Linan Yue; Weibo Gao; |
| 50 | ColorBench: Benchmarking Mobile Agents with Graph-Structured Framework for Complex Long-Horizon Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To bridge the gap between offline and online evaluation and enhance testing stability, this paper introduces a novel graph-structured benchmarking framework. |
Yuanyi Song; Heyuan Huang; Qiqiang Lin; Yin Zhao; Xiangmou Qu; Jun Wang; Xingyu Lou; Weiwen Liu; Zhuosheng Zhang; Jun Wang; Zhaoxiang Wang; Yong Yu; Weinan Zhang; |
| 51 | Controllable Graph Generation with Diffusion Models Via Inference-Time Tree Search Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inference-time guidance methods mitigate these issues by adjusting the sampling process without retraining, but they remain inherently local, heuristic, and limited in controllability. To overcome these limitations, we propose TreeDiff, a Monte Carlo Tree Search (MCTS) guided dual-space diffusion framework for controllable graph generation. |
Jiachi Zhao; Zehong Wang; Yamei Liao; Chuxu Zhang; Yanfang Ye; |
| 52 | Towards A Universal Graph Structural Encoder Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Additionally, most existing models struggle to capture the rich topological complexity of graph structures, leading to inadequate exploration of the graph embedding space. To address these challenges, we propose GFSE, a universal pre-trained graph encoder designed to capture transferable structural patterns across diverse domains such as the web graph, social networks, and citation networks. |
Jialin Chen; Haolan Zuo; Haoyu Wang; Siqi Miao; Pan Li; Rex Ying; |
| 53 | Modeling Cascaded Delay Feedback for Online Net Conversion Rate Prediction: Benchmark, Insights and Solutions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We further analyze CASCADE and derive three key insights: (1) NetCVR exhibits clear temporal patterns necessitating online continuous modeling; (2) Cascaded modeling CVR and RFR for NetCVR outperforms directly modeling NetCVR; and (3) delay time, which correlated with both CVR and RFR, is an important feature for NetCVR prediction. Based on these insights, we propose neT convErsion caScaded modeLing and debiAsing method (TESLA). |
Mingxuan Luo; Guipeng Xv; Sishuo Chen; Xinyu Li; Li Zhang; Zhangming Chan; Xiang-Rong Sheng; Han Zhu; Jian Xu; Bo Zheng; Chen Lin; |
| 54 | Adaptive Contrastive Learning in Sequential Recommendation Based on Perturbation and Restoration Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These strategies, however, often overlook the inherent semantic similarity between the original sequence and its augmented views, which can inadvertently distort user intent and compromise recommendation accuracy. To address this issue, we propose an Adaptive Contrastive Learning framework for Sequential Recommendation (ACLSRec), which incorporates learnable perturbation and restoration networks for adaptive augmentation. |
Yanbo Zhou; Bin L\{u}; Xu-Hua Yang; Xin-Li Xu; Boling Wang; |
| 55 | MMTableBench: A Multi-level Multimodal Benchmark for Reasoning and Layout Complexity in Table QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To advance TableQA beyond superficial task difficulty and toward interpretable capability modeling, we introduce MMTableBench, a multi-level benchmark that systematically evaluates MLLMs along two fine-grained dimensions: layout complexity and reasoning complexity. |
Xianjie Wu; Xiaohang Xu; Tingyu Jiang; Jian Yang; Di Liang; Xianfu Cheng; Zhenhe Wu; Linzheng Chai; Wei Zhang; Jiaheng Liu; Ge Zhang; Bob Simons; Tongliang Li; Zhoujun Li; |
| 56 | Effective and Unsupervised Social Event Detection and Evolution Via RAG and Structural Entropy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Third, the lack of structured temporal context has hindered the development of effective models for event evolution, limiting users’ access to event information. To address these challenges, we propose a foundation model for unsupervised Social Event Detection and Evolution, namely RagSEDE. |
Qitong Liu; Hao Peng; Zuchen Li; Xihang Meng; Ziyu Yang; Jiting Li; Li Sun; Philip S. Yu; |
| 57 | Adaptive Model and Strategy Routing for Cost-Efficient LLM Services Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A key challenge is therefore how to adaptively allocate models and strategies to achieve high-quality responses under constrained costs. To address this challenge, we propose Route-To-Reason (RTR), a unified routing framework that simultaneously selects suitable LLMs and reasoning strategies according to query complexity and user budget. |
Zhihong Pan; Kai Zhang; Yuze Zhao; Yupeng Han; |
| 58 | OFA-MAS: One-for-All Multi-Agent System Topology Design Based on Mixture-of-Experts Graph Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This approach suffers from poor generalization to unseen domains and fails to leverage shared structural knowledge across different tasks. To address this, we propose OFA-MAS, a one-for-all framework that generates adaptive collaboration graphs for any task described in natural language through a single universal model. |
Shiyuan Li; Yixin Liu; Yu Zheng; Mei Li; Quoc Viet Hung Nguyen; Shirui Pan; |
| 59 | DyLogNet: A Dynamic Multi-Relational Graph Framework for Log Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, most prior works build a single-view representation, overlooking the multi-relational nature of logs. To overcome these challenges, we propose DyLogNet, a dynamic multi-relational graph framework for log anomaly detection. |
Xudong Zhao; Xiaolong Xu; Haolong Xiang; Tong Gao; Lianyong Qi; Amin Beheshti; Xuyun Zhang; Wanchun Dou; |
| 60 | They Said Memes Were Harmless — We Found The Ones That Hurt: Decoding Jokes, Symbols, and Cultural References Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Prior approaches, from fusion-based methods to in-context learning with Large Vision-Language Models (LVLMs), have made progress but remain limited by three factors: i) cultural blindness (missing symbolic context), ii) boundary ambiguity (satire vs. abuse confusion), and iii) lack of interpretability (opaque model reasoning). We introduce CROSS-ALIGN+, a three-stage framework that systematically addresses these limitations: (1) Stage I mitigates cultural blindness by enriching multimodal representations with structured knowledge from ConceptNet, Wikidata, and Hatebase; (2) Stage II reduces boundary ambiguity through parameter-efficient LoRA adapters that sharpen decision boundaries; and (3) Stage III enhances interpretability by generating cascaded explanations. |
Sahil Tripathi; Gautam Siddharth Kashyap; Mehwish Nasim; Jian Yang; Jiechao Gao; Usman Naseem; |
| 61 | Identification of Influential Node Group in Attributed Graph Through Explaining Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce Global Graph UNderstanding (GGUN), a perturbation-based framework leveraging the explanatory power of Graph Neural Networks. |
Xiao Tan; Tongtong Su; Jiayi Wu; Yan Zhang; Binghui Xu; Dian Shen; Meng Wang; Beilun Wang; |
| 62 | SketchMind: Understanding Abstract Sketches with MLLMs for Fine-Grained Sketch-Based Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Changxing Li; Donglin Zhang; Zhikai Hu; Xiao-Jun Wu; Josef Kittler; |
| 63 | Byte-token Enhanced Language Models for Temporal Point Processes Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, traditional TPP models often struggle to effectively incorporate the rich textual descriptions that accompany these events, while Large Language Models (LLMs), despite their remarkable text processing capabilities, lack mechanisms for handling the temporal dynamics inherent in Web-based event sequences. To bridge this gap, we introduce Language-TPP, a unified framework that seamlessly integrates TPPs with LLMs for enhanced Web event sequence modeling. |
Quyu Kong; Yixuan Zhang; Yang Liu; Panrong Tong; Enqi Liu; Feng Zhou; |
| 64 | Multi-Source Unsupervised Graph Domain Adaptation Via Concise Propagation–Transformation Pipeline Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (C2) Model-level: Many UGDA models emphasize complex, handcrafted Graph neural network (GNN) architectures, while simpler yet effective designs with propagation (P) \& transformation (T) pipeline remain underexplored. To address these challenges, in this paper, we propose a novel approach, which leverages Concise Propagation–Transformation pipeline for multi-source unsupervised Graph Domain Adaptation, dubbed as CPT-GDA, to better capture complementary knowledge from multiple sources in an efficient manner. |
Jiayi Wang; Yi Li; Xin Zheng; Junyang Chen; Yanqing Guo; Alan Wee-Chung Liew; Shirui Pan; |
| 65 | Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ProtoFed-SP, a prompt-based framework that injects dual-timescale soft prompts into a frozen backbone: a fast, sparse short-term prompt tracks session intent, while a slow long-term prompt is anchored to a small server-side prototype library that is continually refreshed via differentially private federated aggregation. |
Canran Xiao; Liwei Hou; |
| 66 | Navigating Truth in Multimodal Fact-checking Via Retrieval- and Reasoning-Enhanced Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing MLLM-based fact-checking methods fail to fully exploit visual evidence, and their reliance on rigid fine-tuning templates limits context-aware explanations and leads to weak deep reasoning. To address these limitations, we propose FACTCOMPASS, a novel framework that combines reasoning-aware fine-tuning with large-scale rule-based reinforcement learning and incorporates a semantic- and knowledge-enhanced retrieval module to strengthen deep reasoning and improve evidence utilization. |
Fanrui Zhang; Qiang Zhang; Jianwen Sun; Chuanhao Li; Jiaxin Ai; Yukang Feng; Zizhen Li; Kaipeng Zhang; Jiawei Liu; Zheng-Jun Zha; |
| 67 | Exploring and Exploiting Security Vulnerabilities in Self-Hosted LLM Services Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: From a systematic perspective, we propose LENS, a framework that explores and exploits vulnerabilities in self-hosted LLM services for comprehensive security evaluation. |
Zhihuang Liu; Ling Hu; Yonghao Tang; Tongqing Zhou; Fang Liu; Zhiping Cai; |
| 68 | CCAF: Coarse-to-fine Cross-Modal Alignment and Fusion for Multimodal Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, the cross-modal alignment and fusion of complex global and local cross-modal information pose significant challenges in MSA tasks. To address this issue, we propose a novel MSA framework that simultaneously captures coarse-grained and fine-grained cross-modal sentiment cues through global and local cross-modal alignment and fusion. |
Xianbing Zhao; Shengzun Yang; Buzhou Tang; |
| 69 | Detecting Miscitation on The Scholarly Web Through LLM-Augmented Text-Rich Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While large language models (LLMs) offer powerful capabilities in semantic reasoning for this task, their deployment is hindered by hallucination risks and high computational costs. In this work, we introduce LLM-Augmented Graph Learning-based Miscitation Detector (LAGMiD), a novel framework that leverages LLMs for deep semantic reasoning over citation graphs and distills this knowledge into graph neural networks (GNNs) for efficient and scalable miscitation detection. |
Huidong Wu; Haojia Xiang; Jingtong Gao; Xiangyu Zhao; Dengsheng Wu; Jianping Li; |
| 70 | Graph-to-Tree: Topological Decomposition for Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Every graph hides a tree: through tree decomposition—a foundational tool in modern graph theory with broad applications such as in computational power networks, any network can be unfolded into a hierarchy of overlapping vertex bags whose backbone is a tree. Leveraging this powerful lens, we propose Topological Decomposition for Self-supervised Learning (TopDSL), a framework that injects multi-scale signals into graph representation learning. |
Yejiang Wang; Yuhai Zhao; Fangting Li; Jiapu Wang; Meixia Wang; Ling Li; Miaomiao Huang; Zhengkui Wang; Shirui Pan; |
| 71 | From Token to Item: Enhancing Large Language Models for Recommendation Via Item-aware Attention Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we revisit the role of tokens in LLM-driven recommendation and categorize their relations into two types: (1) intra-item token relations, which present the content semantics of an item, e.g., name, color, and size; and (2) inter-item token relations, which encode collaborative relations across items. |
Xiaokun Zhang; Bowei He; Jiamin Chen; Ziqiang Cui; Chen Ma; |
| 72 | Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our empirical analysis corroborates this challenge and further uncovers a recurring behavioral pattern in long sequences, which we term the session hopping phenomenon: while user interests remain stable within a short temporal span, referred to as a session, they often exhibit drastic shifts across sessions and may reappear after multiple sessions. To address this challenge, we propose the Mixture of Sequence (MoS) framework, a model-agnostic MoE approach that achieves accurate predictions by extracting theme-specific and multi-scale subsequences from noisy raw user sequences. |
Xiao Lin; Zhicheng Tang; Weilin Cong; Mengyue Hang; Kai Wang; Yajuan Wang; Zhichen Zeng; Ting-Wei Li; Hyunsik Yoo; Zhining Liu; Xuying Ning; Ruizhong Qiu; Wen-Yen Chen; Shuo Chang; Rong Jin; Huayu Li; Hanghang Tong; |
| 73 | PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent approaches leverage large language models (LLMs) to extract fine-grained semantic entities and enhance semantic matching, but they typically treat entities as independent fragments, overlooking the multi-faceted nature of scientific concepts. To address this limitation, we propose Pairwise Semantic Matching (PairSem), a framework that represents relevant semantics as entity–aspect pairs, capturing complex, multi-faceted scientific concepts. |
Wonbin Kweon; Runchu Tian; Seongku Kang; Pengcheng Jiang; Zhiyong Lu; Jiawei Han; Hwanjo Yu; |
| 74 | Multi-view Semantic Contrastive Alignment for Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Jiuqiang Li; Hongjun Wang; |
| 75 | Enhancing Large Language Models for Time-Series Forecasting Via Vector-Injected In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Thus, LLM4TSF faces a dual challenge of prediction performance and compute overhead. To address this, we aim to explore a method for improving the forecasting performance of LLM4TSF while freezing all LLM parameters to reduce computational overhead. |
Jianqi Zhang; Jingyao Wang; Wenwen Qiang; Fanjiang Xu; Changwen Zheng; |
| 76 | Evidential Matching, Uncertainty Calibration: Towards Robust Composed Video Retrieval with Noisy Triplets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CURE, an evidential Cross-modal Uncertainty calibRation framEwork that explicitly models and exploits alignment uncertainty to make CVR robust to NTC. |
Zhangtao Cheng; Bozhu Zheng; Ting Zhong; Fan Zhou; |
| 77 | To Search or Not to Search: Aligning The Decision Boundary of Deep Search Agents Via Causal Intervention Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This causes over-search (redundant searching despite sufficient knowledge) and under-search (premature termination yielding incorrect answers). To address these errors, we propose a comprehensive framework comprising two key components. |
Wenlin Zhang; Kuicai Dong; Junyi Li; Yingyi Zhang; Xiaopeng Li; Pengyue Jia; Yi Wen; Derong Xu; Maolin Wang; Yichao Wang; Yong Liu; Xiangyu Zhao; |
| 78 | HingeMem: Boundary Guided Long-Term Memory with Query Adaptive Retrieval for Scalable Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose HingeMem, a boundary-guided long-term memory that operationalizes event segmentation theory to build an interpretable indexing interface via boundary-triggered hyperedges over four elements: person, time, location, and topic. |
Yijie Zhong; Yunfan Gao; Haofen Wang; |
| 79 | GenCI: Generative Modeling of User Interest Shift Via Cohort-based Intent Learning for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By scoring each candidate in isolation, CTR models discard the rich contextual signal implied by the recalled set as a whole, leading to a misalignment where long-term preferences often override the user’s immediate, evolving intent. To address these issues, we propose GenCI, a generative user intent framework that leverages semantic interest cohorts to model dynamic user preferences for CTR prediction. |
Kesha Ou; Zhen Tian; Wayne Xin Zhao; Hongyu Lu; Ji-Rong Wen; |
| 80 | Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite their clear synergistic potential, where deduction can validate hypotheses and abduction can uncover deeper logical patterns, existing methods address them in isolation. To bridge this gap, we propose DARK, a unified framework for Deductive and Abductive Reasoning in Knowledge graphs. |
Yisen Gao; Jiaxin Bai; Yi Huang; Xingcheng Fu; Qingyun Sun; Yangqiu Song; |
| 81 | Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, leveraging this data poses significant challenges due to its vast volume, ambiguity, and confluence of diverse perspectives. To tackle these challenges, we propose a novel Large Language Model (LLM) Agent to automate online review mining for segregation prediction. |
Bingbing Fan; Lin Chen; Songwei Li; Jian Yuan; Fengli Xu; Pan Hui; Yong Li; |
| 82 | A Long-term Value Prediction Framework In Video Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present a practical ranking-stage LTV framework that systematically addresses three core challenges: position bias, attribution ambiguity, and temporal limitations. |
Huabin Chen; Xinao Wang; Huiping Chu; Keqin Xu; Chenhao Zhai; Chenyi Wang; Kai Meng; Yuning Jiang; |
| 83 | Probe-and-Fetch: Dynamic KV Cache Pruning for Accelerated Long-Context Inference in Web-Scale AI Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This challenge, however, introduces a classic chicken-and-egg problem: the model cannot foresee the necessary KV entries for attention without first inferring on the content, yet doing so on the full context is prohibitively expensive. This paper introduces P&F, a unified framework that resolves this dilemma through a core ”probe-and-fetch” mechanism, which ingeniously integrates with speculative decoding — an acceleration approach already adopted in web-scale AI search. |
Yuchen Li; Rui Kong; Xinran Chen; Chengzhe Zhang; Jiamin Chen; Cheng Deng; Xinyu Ma; Haojie Zhang; Tianhao Peng; Hengyi Cai; Shuaiqiang Wang; Jiashu Zhao; Yongqi Zhang; Haoyi Xiong; Jimmy Xiangji Huang; Lei Chen; Jun Wang; Dawei Yin; |
| 84 | STPWR: A Spatiotemporal Prediction-based Worker Pre-Recruitment Framework for Mobile Crowd Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Conventional methods typically recruit workers only after a task is generated, leading to significant response delays, especially for time-sensitive tasks. To address this limitation, we propose the Spatiotemporal Task Prediction-based Worker Pre-Recruitment (STPWR) framework. |
Guisong Yang; Yuchen Yang; Yunbo Shen; Xingyu He; Jianheng Tang; Yunhuai Liu; Chengji Xu; |
| 85 | NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Speculative Decoding (SD) has been proposed to accelerate the autoregressive generation process, existing implementations introduce new bottlenecks: they typically require separate draft models and model-based verifiers, which require additional training and increase latency overhead. In this paper, we address these challenges with NEZHA, a novel architecture that achieves hyperspeed decoding for GR systems without sacrificing recommendation quality. |
Yejing Wang; Shengyu Zhou; Jinyu Lu; Ziwei Liu; Langming Liu; Maolin Wang; Wenlin Zhang; Feng Li; Wenbo Su; Pengjie Wang; Jian Xu; Xiangyu Zhao; |
| 86 | Real or Rogue? Detecting Malicious Miniapps with Deceptive Reporting Interface Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Alarmingly, our study reveals that there are malicious miniapps implementing deceptive reporting interfaces to impersonate the official ones. |
Yuqing Yang; Zhiqiang Lin; |
| 87 | Reading Between The Lines: Towards Reliable Black-box LLM Fingerprinting Via Zeroth-order Gradient Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address this, we first leverage Fisher Information Theory to formally demonstrate that the gradient of the model’s input is a more informative feature for fingerprinting than the output. Based on this insight, we propose ZeroPrint, a novel method that approximates these information-rich gradients in a black-box setting using zeroth-order estimation. |
Shuo Shao; Yiming Li; Hongwei Yao; Yifei Chen; Yuchen Yang; Zhan Qin; |
| 88 | Route-and-Reason: Energy-Efficient Scaling of LLM Reasoning Via Reinforced Model Routing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Collaboration at the level of intermediate reasoning steps could enable more efficient coordination, but it also poses significant challenges for router scheduling, placing immense demands on the quality of task decomposition and the precision of the router. To address this, we propose R2-Reasoner, a novel framework centered around a reinforced model router designed to achieve energy-efficient and scalable LLM reasoning. |
Chenyang Shao; Xinyang Liu; Yutang Lin; Fengli Xu; Yong Li; |
| 89 | OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose Model, a unified Transformer backbone that simultaneously performs user-behavior sequence modeling and feature interaction. |
Zhaoqi Zhang; Haolei Pei; Jun Guo; Tianyu Wang; Yufei Feng; Hui Sun; Shaowei Liu; Aixin Sun; |
| 90 | Tail-Aware Data Augmentation for Long-Tail Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose Tail-Aware Data Augmentation (TADA) for long-tail sequential recommendation, which enhances the interaction frequency for tail items/users while maintaining head performance, thereby promoting the model’s learning capabilities for the tail. |
Yizhou Dang; Zhifu Wei; Minhan Huang; Lianbo Ma; Jianzhe Zhao; Guibing Guo; Xingwei Wang; |
| 91 | Enhancing Federated Class-Incremental Learning Via Spatial-Temporal Statistics Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these methods cannot avoid spatial-temporal client drift caused by data heterogeneity and often incur significant computational and communication overhead, limiting practical deployment. To address these challenges simultaneously, we propose a novel approach, Spatial-Temporal Statistics Aggregation (STSA), which provides a unified framework to aggregate feature statistics both spatially (across clients) and temporally (across stages). |
Zenghao Guan; Guojun Zhu; Yucan Zhou; Wu Liu; Weiping Wang; Jiebo Luo; Xiaoyan Gu; |
| 92 | From Newborn to Impact: Bias-Aware Citation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We identify two key research gaps: (i) insufficient modeling of implicit factors of scientific impact, leading to reliance on coarse proxies; and (ii) a lack of bias-aware learning that can deliver stable predictions on lowly cited papers. We address these gaps by proposing a Bias-Aware Citation Prediction Framework, which combines multi-agent feature extraction with robust graph representation learning. |
Mingfei Lu; Mengjia Wu; Jiawei Xu; Weikai Li; Feng Liu; Ying Ding; Yizhou Sun; Jie Lu; Yi Zhang; |
| 93 | TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological Counseling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These limitations pose risks to digital well-being, as disjointed interactions can fail to support vulnerable users effectively. To address these gaps, we introduce TheraMind, a strategic and adaptive agent designed for trustworthy online longitudinal counseling. |
He Hu; Chiyuan Ma; Qianning Wang; Liu Lin; Yucheng Zhou; Laizhong Cui; Fei Ma; Qi Tian; |
| 94 | FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This poses additional challenges for effectively measuring the cross-domain similarities under heterogeneity. To address these challenges, we propose an LLM-based framework for federated cross-domain recommendation, FeDecider. |
Xinrui He; Ting-Wei Li; Tianxin Wei; Xuying Ning; Xinyu He; Wenxuan Bao; Hanghang Tong; Jingrui He; |
| 95 | PaperAsk: A Benchmark for Reliability Evaluation of LLMs in Paper Search and Reading Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce PaperAsk, a benchmark that systematically evaluates LLMs across four key research tasks: citation retrieval, content extraction, paper discovery, and claim verification. |
Yutao Wu; Xiao Liu; Yunhao Feng; Jiale Ding; Xingjun Ma; |
| 96 | Optimizing Multi-Turn Interactive Recommendation Agents Via Generative Intrinsic Motivation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this process faces three key challenges: credit assignment in multi-turn interactions, efficient exploration in large action spaces, and coordinated learning of multiple interactive skills. To tackle this challenge, we present a new preference optimization paradigm, GIMO, which treats the interaction and learning process of IRAs in sparse environments as the continuous fulfillment and stimulation of three intrinsic drives: Autonomy, Competence, and Relatedness. |
Xueyang Feng; Jiakai Tang; Xu Chen; Quanyu Dai; Zhenhua Dong; |
| 97 | MultiFPT: Towards Multi-Attribute Fairness in Pre-Trained Graph Neural Networks Via Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, incorporating extra fairness constraints into pre-trained GNNs usually requires full model retraining, which is computationally expensive and often impractical. To address these limitations, we propose a novel Multi-attribute Fairness-aware Prompt Tuning framework named MultiFPT. |
Meng Cao; Mingcai Chen; Shuangjie Li; Hualei Yu; Shuai Feng; Demin Gao; |
| 98 | Towards Token-Level Text Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We formally define text anomalies at both document and token-levels, and propose a unified detection framework that operates across multiple levels. |
Yang Cao; Bicheng Yu; Sikun Yang; Ming Liu; Yujiu Yang; |
| 99 | Causal Pre-training Under The Fairness Lens: An Empirical Study of TabPFN Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the fairness properties of these foundational models, which incorporate ideas from causal reasoning during pre-training, remain underexplored. In this work, we conduct a comprehensive empirical evaluation of TabPFN and its fine-tuned variants, assessing predictive performance, fairness, and robustness across varying dataset sizes and distributional shifts. |
Qinyi Liu; Mohammad Khalil; Naman Goel; |
| 100 | From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Meanwhile, most detection models lack fine-grained attribution, limiting interpretability and practical utility. To address these gaps, we introduce WildFakeBench, a large-scale benchmark of over 10,000 real-world micro-videos covering diverse misinformation types and sources, each annotated with expert-defined attribution labels. |
Zhi Zeng; Yifei Yang; Jiaying Wu; Xulang Zhang; Xiangzheng Kong; Herun Wan; Zihan Ma; Minnan Luo; |
| 101 | Not All Candidates Are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We further show that the common practice of uniformly scaling model complexity across all samples is inefficient, as it overspends computation on easy cases and slows training without proportional gains. To address these limitations, this paper presents Heterogeneity-Aware Adaptive Pre-ranking (HAP), a unified framework that mitigates gradient conflicts through conflict-sensitive sampling coupled with tailored loss design, while adaptively allocating computational budgets across candidates. |
Pengfei Tong; Siyuan Chen; Chenwei Zhang; Bo Wang; Qi Pi; Pixun Li; Zuotao Liu; |
| 102 | Unleashing The Potential of Sparse Attention on Long-term Behaviors for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This is because user behaviors exhibit personalization and temporal characteristics: different users have distinct behavior patterns, and these patterns change over time, with data from these users differing significantly from data in other fields in terms of distribution. To address these challenges, we propose SparseCTR, an efficient and effective model specifically designed for long-term behaviors of users. |
Weijiang Lai; Beihong Jin; Di Zhang; Siru Chen; Jiongyan Zhang; Yuhang Gou; Jian Dong; Xingxing Wang; |
| 103 | MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing TKG-based LLM reasoning methods still struggle with four major challenges: maintaining temporal faithfulness in multi-hop reasoning, achieving multi-entity temporal synchronization, adapting retrieval to diverse temporal operators, and reusing prior reasoning experience for stability and efficiency. To address these issues, we propose MemoTime, a memory-augmented temporal knowledge graph framework that enhances LLM reasoning through structured grounding, recursive reasoning, and continual experience learning. |
Xingyu Tan; Xiaoyang Wang; Qing Liu; Xiwei Xu; Xin Yuan; Liming Zhu; Wenjie Zhang; |
| 104 | RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models Via Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work,we propose RAG-GFM, a Retrieval-Augmented Generation aided Graph Foundation Model that offloads knowledge from parameters and complements parameterized learning. |
Haonan Yuan; Qingyun Sun; Jiacheng Tao; Xingcheng Fu; Jianxin Li; |
| 105 | From Prediction to Understanding: Leveraging Reasoning in Large Language Model-based Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, as the output of LLMs consists solely of recommended items, the resulting recommendations lack explainability. To address these issues, we propose RE2, which enables LLMs to explicitly generate reasoning content before providing recommendations. |
Zhi-Yuan Chen; Siyu Lu; Qiang Liu; Xingxing Wang; Yankai Lin; |
| 106 | FraudShield: Knowledge Graph Empowered Defense for LLMs Against Fraud Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although advanced defense methods have been developed to address this issue, they often exhibit limitations in effectiveness, interpretability, and generalizability, particularly when applied to LLM-based applications. To address these challenges, we introduce FraudShield, a novel framework designed to protect LLMs from fraudulent content by leveraging a comprehensive analysis of fraud tactics. |
Naen Xu; Jinghuai Zhang; Ping He; Chunyi Zhou; Jun Wang; Zhihui Fu; Tianyu Du; Zhaoxiang Wang; Shouling Ji; |
| 107 | Inference Cost Attacks for Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce the Retrieval-Augmented Inference Cost Attack (RA-ICA), a novel attacking paradigm that targets the computational cost of RAG-enhanced LLM systems by injecting malicious documents into external knowledge corpus. |
Chengliang Liu; Liangbo Ning; Yujuan Ding; Wenqi Fan; |
| 108 | CIFAD: Causal-Invariant Subspace Learning for Few-Shot Anomaly Detection on Dynamic Relational Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, these methods are highly dependent on inherent labels and fail to detect common few-shot anomalies in social networks. To address these issues, we propose CIFAD, a Causal-Invariant Few-shot Anomaly Detection method that improves few-shot anomaly detection with an active annotation strategy. |
Yu Xiao; Haolong Xiang; Xiaolong Xu; Lianyong Qi; Xuyun Zhang; Wei Fan; Wanchun Dou; |
| 109 | C2-SFL:Class-Balanced and Cost-Aware Split Federated Learning for Mobile Edge Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing client selection and resource allocation strategies fail to account for data and system heterogeneity, thereby exacerbating data skew and leading to inefficient training and high costs. To address these issues, we propose a Class-Balanced and Cost-Aware SFL (C2-SFL) framework that jointly optimizes client selection, model partitioning, and bandwidth allocation. |
Kai Cheng; Zhengyu Zhang; Tong Wu; Huan Zhou; Xinggang Fan; |
| 110 | DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, we propose a novel black-box attack framework named DrunkAgent. |
Shiyi Yang; Zhibo Hu; Xinshu Li; Chen Wang; Tong Yu; Xiwei Xu; Liming Zhu; Lina Yao; |
| 111 | Talos: Optimizing Top-K Accuracy in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In addition, RS often suffer from distribution shifts due to evolving user preferences or data biases, further complicating the task. To address these issues, we propose Talos, a loss function that is specifically designed to optimize the Top-K recommendation accuracy. |
Shengjia Zhang; Weiqin Yang; Jiawei Chen; Peng Wu; Yuegang Sun; Gang Wang; Qihao Shi; Can Wang; |
| 112 | ‘They’ve Stolen My GPL-Licensed Model!’: Toward Standardized and Transparent Model Licensing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Meanwhile, the reused assets may also be under free-content licenses and model licenses, which pose a potential risk of license noncompliance and rights infringement within the model production workflow. In this paper, we address these challenges along two lines: 1) For ML workflow compliance, we propose ModelGo (MG) Analyzer, a tool that incorporates a vocabulary for ML workflow management and encoded license rules, enabling ontological reasoning to analyze rights granting and compliance issues. |
Moming Duan; Rui Zhao; Linshan Jiang; Nigel Shadbolt; Bingsheng He; |
| 113 | Generative Data Transformation: From Mixed to Unified Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing prevailing model-centric paradigm – which relies on complex, customized architectures – struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose Taesar, a data-centric framework for target-aligned sequential regeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. |
Jiaqing Zhang; Mingjia Yin; Hao Wang; Yuxin Tian; Yuyang Ye; Yawen Li; Wei Guo; Yong Liu; Enhong Chen; |
| 114 | Bias Mitigation for Harmful Meme Detection Using Front-door Adjustment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they neglect latent data bias, resulting in incorrect predictions based on spurious label-specific features. To remedy this limitation, we propose CausalHM, a causally grounded framework that debiases harmful meme detection via front-door adjustment. |
Jiangfeng Zeng; Cheng Xiong; Xiao Ma; Jialin Qin; |
| 115 | Hi-GMAE: Hierarchical Graph Masked Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Therefore, the inability of single-scale GMAE models to incorporate these hierarchical relationships often results in an inadequate capture of crucial high-level graph information, leading to a noticeable decline in performance. To address this limitation, we propose Hierarchical Graph Masked AutoEncoders (Hi-GMAE), a novel multi-scale GMAE framework designed to handle the hierarchical structures within graphs. |
Chuang Liu; Zelin Yao; Xueqi Ma; Mukun Chen; Luzhi Wang; Jia Wu; Wenbin Hu; |
| 116 | What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, \& Emerging Implications of Agentic E-Commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We develop ACES, a sandbox environment that pairs a platform-agnostic agent with a fully programmable mock marketplace to study this. |
Amine Allouah; Omar Besbes; Josu\'{e} D. Figueroa; Yash Kanoria; Akshit Kumar; |
| 117 | Joint Similar User Exploration and Informative Behavior Guidance for Multi-Modal New Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we focus on the Multi-Modal New Item Recommendation (MMNIR) problem, where items with multi-modal content but newly introduced items lack interaction history. |
Jianye Xie; Lianyong Qi; Weiming Liu; Anqi Wang; Xiaolong Xu; Haolong Xiang; Xuyun Zhang; Wenwen Gong; Yang Zhang; Amin Beheshti; Wanchun Dou; |
| 118 | Audit-of-Audits for The Web: Bayesian Meta-Evaluation That Yields Interval-Valued, Threshold-Aligned Fairness Claims Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a Bayesian audit-of-audits that pools heterogeneous audits—count-based and metric-only—into interval-valued fairness claims with explicit uncertainty and policy-risk tables aligned to practitioner thresholds. |
Dandan Liu; Aznul Qalid Md Sabri; Lihu Pan; Guangrui Fan; |
| 119 | Task-Aware Cloud-End Offloading for Vision-Language Model Serving Via Dynamic Modality-Specific Adapter Scheduling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, cloud-centric serving often suffers from high latency, rising costs, and network dependency, while purely on-device deployment is constrained by limited memory and reduced accuracy on complex tasks. To address this accuracy–latency–cost trilemma, we propose ShiftVL, a task-aware end–cloud serving framework that shifts suitable execution to the end device with a cloud fallback. |
Zian Wang; Ziyi Wang; Jie Xing; Yaya Wei; Ziyan Zhong; Lanshan Zhang; |
| 120 | Quantum-enhanced Representation Learning and Matching Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper investigates why and how quantum computing can be integrated into recommender systems. |
Anchen Li; Elena Casiraghi; |
| 121 | ARADD: An Automatic Real-World API Discovery and Deployment Framework for AI Guide Service in Baidu Map Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose an Automatic Real-world API Discovery and Deployment (ARADD) framework to efficiently discover new real-world APIs suitable for query solving and automatically master them with minimal labor cost. |
Fuling Wang; Le Zhang; Jingbo Zhou; Jindong Han; Ying Sun; Chuan Qin; Hengshu Zhu; Hui Xiong; |
| 122 | AIMER: Affective Intention-guided Multimodal Emotion Reasoner for Visual Emotion Analysis in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing visual emotion analysis approaches primarily rely on surface-level visual cues and fail to capture the intrinsic meaning of emotional expressions. To overcome this limitation, we propose AIMER, a framework that integrates both the perceptual and intentional aspects of emotion. |
Yubeen Lee; Shinyu Park; Jiwon Park; Eunil Park; |
| 123 | Emergent Coordinated Behaviors in Networked LLM Agents: Modeling The Strategic Dynamics of Information Operations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. |
Gian Marco Orlando; Jinyi Ye; Valerio La Gatta; Mahdi Saeedi; Vincenzo Moscato; Emilio Ferrara; Luca Luceri; |
| 124 | Federated Latent Factor Learning for Privacy-Preserving Spatio-Temporal Signal Recovery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the existing LFL models require the collected sensing signals to be maintained in one central place like a central server, which is becoming unacceptable for data owners who are getting increasingly privacy-sensitive. To address this issue, this paper innovatively proposes a f ederated l atent f actor l earning (FLFL) model for privacy-preserving spatio-temporal signal recovery. |
Chengjun Yu; Di Wu; Yi He; Jia Chen; Xin Luo; |
| 125 | Verifiable Federated Representation Learning for Cross-domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose VeriFRL, a verifiable federated representation learning framework for cross-domain sequential recommendation. |
Tao Tang; Botao Liu; Ciyuan Peng; Ivan Lee; Xiangjie Kong; |
| 126 | FairFRL: Fairness-aware Federated Representation Learning for Cross-domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such an imbalance degrades recommendation accuracy and raises fundamental fairness concerns. To address these challenges, we propose FairFRL, a fairness-aware federated representation learning framework designed to mitigate contribution imbalance under dynamic cross-domain drift. |
Tao Tang; Mujie Liu; Botao Liu; Wei Du; Liping Chen; Xinrui Cheng; Jiaxin Du; Xiangjie Kong; |
| 127 | COINS: Semantic Ids Enhanced Cold Item Representation for Click-through Rate Prediction in E-commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these methods fail to account for the asymmetry between collaboration and content, nor the fine-grained differences among items. To address these issues, we propose COINS, an item representation enhancement approach based on fused alignment of semantic IDs. |
Qihang Zhao; Zhongbo Sun; Xiaoyang Zheng; Xian Guo; Siyuan Wang; Zihan Liang; Mingcan Peng; Ben Chen; Chenyi Lei; |
| 128 | GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing GraphRAG methods still face significant bottlenecks when handling complex problems that require multi-hop reasoning, as their query and retrieval phases are largely based on pre-defined heuristics and do not fully utilize the reasoning potentials of LLMs. To address this problem, we propose GraphRAG-R1, an adaptive GraphRAG framework by training LLMs with process-constrained outcome-based reinforcement learning (RL) to enhance the multi-hop reasoning ability. |
Chuanyue Yu; Kuo Zhao; Yuhan Li; Heng Chang; Mingjian Feng; Xiangzhe Jiang; Yufei Sun; Jia Li; Yuzhi Zhang; Qingyun Sun; Jianxin Li; Ziwei Zhang; |
| 129 | Anchor Drift No More: Hierarchical Consistency-Guided Prompt Distillation for Incomplete Multimodal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce HiCoD (Hierarchical Consistency-Guided Pro mpt Distillation), which learns a robust, class-anchored semantic space. |
Ruiting Dai; Zesen Cai; Lisi Mo; Guiduo Duan; Keren Shi; Tao He; |
| 130 | Evolving Proxy Kills Drift: Data-Efficient Streaming Time Series Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These methods thus fail to work well on streaming time series with changing data distributions and anomaly formats. To contend with such streaming time series and to accommodate memory constraints, we propose the first data-efficient streaming time series anomaly detection framework, called DESS. |
Qing Wei; Hao Miao; Yan Zhao; Kai Zheng; Bin Yang; Volker Markl; Christian S. Jensen; |
| 131 | MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose MemWeaver, a framework that weaves the user’s entire textual history into a hierarchical memory to power deeply personalized generation. |
Shuo Yu; Mingyue Cheng; Daoyu Wang; Qi Liu; Zirui Liu; Ze Guo; Xiaoyu Tao; |
| 132 | Source Localization in Continuous-Time Propagation Via Spectral ODE Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Source localization has attracted increasing attention in recent years due to its vital role in governing the harmful propagation. However, existing localization methods do not … |
Dongpeng Hou; Yuchen Wang; Giulio Cimini; Roberto Benzi; Huixiang Zhang; Zhen Wang; Chao Gao; |
| 133 | Spiking Graph Predictive Coding for Reliable OOD Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Achieving reliable OOD generalization demands principled and interpretable uncertainty estimation; however, existing methods are largely post-hoc, insensitive to distribution shifts, and unable to explain where uncertainty arises especially in high-stakes settings. To address these limitations, we introduce SpIking GrapH predicTive coding (SIGHT ), an uncertainty-aware plug-in graph learning module for reliable OOD Generalization. |
Jing Ren; Jiapeng Du; Bowen Li; Ziqi Xu; Xin Zheng; Hong Jia; Suyu Ma; Xiwei Xu; Feng Xia; |
| 134 | When to Invoke: Refining LLM Fairness with Toxicity Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This raises a key question that existing approaches often overlook: when should corrective mechanisms be invoked to ensure fair and reliable assessments? To address this, we propose FairToT, an inference-time framework that enhances LLM fairness through prompt-guided toxicity assessment. |
Jing Ren; Bowen Li; Ziqi Xu; Renqiang Luo; Shuo Yu; Xin Ye; Haytham Fayek; Xiaodong Li; Feng Xia; |
| 135 | From Native Memes to Global Moderation: Cross-Cultural Evaluation of Vision–Language Models for Hateful Meme Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a systematic evaluation framework designed to diagnose and quantify the cross-cultural robustness of state-of-the-art VLMs across multilingual meme datasets, analyzing three axes: (i) learning strategy (zero-shot vs. one-shot), (ii) prompting language (native vs. English), and (iii) translation effects on meaning and detection. |
Mo Wang; Kaixuan Ren; Pratik Jalan; Ahmed Ashraf; Tuong Vy Vu; Rahul Seetharaman; Shah Nawaz; Usman Naseem; |
| 136 | Cross-Domain Fake News Detection on Unseen Domains Via LLM-Based Domain-Aware User Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nevertheless, two key challenges arise: (1) how to capture high-level semantics from both news content and user engagements using LLMs; and (2) how to make LLM-generated features more reliable and transferable for CD-FND on unseen domains. To tackle these challenges, we propose DAUD, a novel LLM-based Domain-Aware framework for fake news detection on Unseen Domains. |
Xuankai Yang; Yan Wang; Jiajie Zhu; Pengfei Ding; Hongyang Liu; Xiuzhen Zhang; Huan Liu; |
| 137 | Node Role-Guided LLMs for Dynamic Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: They lack interpretability in their clustering decisions and fail to provide semantic explanations of why clusters form or how they evolve, severely limiting their use in safety-critical domains such as healthcare or transportation. To address these limitations, we propose an end-to-end interpretable framework that maps continuous graph embeddings into discrete semantic concepts through learnable prototypes. |
Dongyuan Li; Ying Zhang; Yaozu Wu; Renhe Jiang; |
| 138 | Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, ride-hailing forecasting faces significant challenges due to geospatial heterogeneity and high susceptibility to external events. This paper proposes MVGR-Net (Multi-View Geospatial Representation Learning), a novel framework that addresses these challenges through a two-stage approach. |
Xixuan Hao; Guicheng Li; Daiqiang Wu; Xusen Guo; Yumeng Zhu; Zhichao Zou; Peng Zhen; Yao Yao; Yuxuan Liang; |
| 139 | Cross-city Time Series Forecasting with Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose xRAG4TS, a novel framework that integrates Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) for cross-city time series forecasting. |
Yue Jiang; Chenxi Liu; Yile Chen; Qin Chao; Shuai Liu; Cheng Long; Gao Cong; |
| 140 | Multimodal Trajectory Representation Learning for Travel Time Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This overlooks the inherent variability of real-world motion patterns, often resulting in information loss and redundancy. To address these challenges, this paper introduces the Multimodal Dynamic Trajectory Integration (MDTI) framework–a novel multimodal trajectory representation learning approach that integrates GPS sequences, grid trajectories, and road network constraints to enhance the performance of TTE. |
Zhi Liu; Xuyuan Hu; Xiao Han; Zhehao Dai; Zhaolin Deng; Guojiang Shen; Xiangjie Kong; |
| 141 | Disentangled Graph LLM for Molecule Graph Editing Under Distribution Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this problem remains challenging, given that the invariant and variant factors are deeply entangled within the editing models. To tackle this challenge, we propose MoFE, a disentangled graph large language model for molecule graph editing that handles editing instructions under distribution shifts via disentangling invariant factors that govern editing-relevant properties. |
Yang Yao; Xin Wang; Yuan Meng; Zeyang Zhang; Hong Mei; Wenwu Zhu; |
| 142 | Agent-Enhanced Heterogeneous Graph RAG for Academic Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing retrieval-augmented generation (RAG) systems struggle in this setting due to three limitations: (1) fixed retrieval strategies that do not adapt to varying query complexity, (2) the absence of sufficiency evaluation leading to incomplete or misaligned evidence, and (3) a lack of structured verification against graph facts. To address these issues, we propose an agentic heterogeneous graph RAG method that transforms the three core stages of the RAG pipeline into explicit agentic decision steps. |
Runsong Jia; Mengjia Wu; Ying Ding; Jie Lu; Yi Zhang; |
| 143 | Be Responsible in Your Answers! Monitoring Out-of-Domain Behaviors in Domain-Specific LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose an innovative LLM domain monitoring framework named DomainMonitor. |
Boquan Li; Chenzhe Lou; Zhe Ren; Peixin Zhang; Zirui Fu; Jun Sun; Yaowen Zheng; |
| 144 | The Devil Within, The Cure Without: Securing Locally Private Graph Learning Under Poisoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our approach targets the full-privacy setting, where both node features and edges are LDP-protected, and executes coordinated manipulations that significantly degrade utility, such as node classification accuracy, across multiple social network benchmarks. To counter these threats, we propose CureNet, a defense framework with four key components: (1) local data perturbation for privacy, (2) trimmed screening to filter abnormal submissions, (3) privacy-aware fake node pruning to remove sophisticated adversaries, and (4) a utility enhancement module to recover graph learning performance under privacy constraints. |
Longzhu He; Peng Tang; Li Sun; Sen Su; |
| 145 | SEAR: LLM-Powered Sequential Recommendation Via Fusion of Collaborative, Semantic, and Rating Information Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce SEAR, an LLM-powered Sequential recommEndation framework via fusion of collAborative, semantic, and Rating information. |
Wei Guan; Jian Cao; Qiqi Cai; Jianqi Gao; Jinyu Cai; See-Kiong Ng; |
| 146 | LLM-enhanced Federated Graph Learning with Geometry-aware Graph Projection and Shared Subspace Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, directly applying LLMs to FGL raises two key challenges: (1) enhancing LLM interpretation of graphs with limited client data, and (2) obtaining a generalizable graph projector across heterogeneous client tasks. To address these challenges, we propose a LLM-enhanced federated graph learning framework FedLGS, which consists of two modules, i.e., geometry-aware graph projection (GGP) and shared subspace aggregation (SSA). |
Pengyang Zhou; Zhihao Huang; Jiahe Xu; Wu Wen; Xiaolin Zheng; Chaochao Chen; Jianwei Yin; |
| 147 | Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Yanlin Song; Ben Liu; V\'{\i}ctor Guti\'{e}rrez-Basulto; Zhiwei Hu; Qianqian Xie; Min Peng; Sophia Ananiadou; Jeff Z. Pan; |
| 148 | PIXEL: Adaptive Steering Via Position-wise Injection with EXact Estimated Levels Under A Subspace Calibration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose Position-wise Injection with eXact Estimated Levels (PIXEL), a position-wise activation steering framework that, in contrast to prior work, learns a property-aligned subspace from dual views (tail-averaged and end-token) and selects intervention strength via a constrained geometric objective with a closed-form solution, thereby adapting to token-level sensitivity without global hyperparameter tuning. |
Manjiang Yu; Hongji Li; Priyanka Singh; Xue Li; Di Wang; Lijie Hu; |
| 149 | SAGE: Global Semantic Alignment with LLMs for Long-Tail Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Semantic Alignment with Global Embedding for Rec ommendation (SAGE-Rec ), a new framework that explicitly leverages global semantic organization from LLMs for sequential recommendation. |
Maolin Wang; Tongshu Bian; Ziyan Wang; Xiaotong Jiang; Binhao Wang; Derong Xu; Wanyu Wang; Ruocheng Guo; Xiangyu Zhao; |
| 150 | ARCHER: Shooting Straight in Multimodal E-Commerce Search at Alibaba with Progressive Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This limitation becomes particularly acute in Business-to-Business environments, where incorrect product recommendations can have significant operational and safety implications. In this paper, we propose a novel solution, Adaptive Retrieval with Category-aware Hierarchical sEmantic Refinement (ARCHER), which presents a novel multimodal retrieval framework that addresses these challenges through progressive semantic alignment. |
Maolin Wang; Lang Fu; Jun Chu; Kai Guo; Chenjie Qin; Xinxin Wang; Siyu Wu; Wen Jiang; Xiangyu Zhao; |
| 151 | Trireme: A Tripartite Regulation Scheme for Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite the urgent demand for mitigation techniques, a significant challenge persists: once a model is distributed for local deployment or fine-tuning, the model provider and third-party regulators relinquish control over the model’s behavior. To address this challenge, we propose a tripartite interactive regulatory scheme (Trireme) to enforce unsafe output prevention for diffusion models. |
Hengtong Zhang; Chen Ye; Hongzhi Wang; |
| 152 | DRGW: Learning Disentangled Representations for Robust Graph Watermarking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Jiasen Li; Yanwei Liu; Zhuoyi Shang; Xiaoyan Gu; Weiping Wang; |
| 153 | We Need A More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a lightweight and robust dual-causal disentanglement framework (DualCD) to enhance the robustness of models under domain incremental scenarios, which can be seamlessly integrated into time series classification models. |
Zhipeng Liu; Peibo Duan; Xuan Tang; Haodong Jing; Mingyang Geng; Yongsheng Huang; Jialu Xu; Bin Zhang; Binwu Wang; |
| 154 | Beyond Denial-of-Service: The Puppeteer’s Attack for Fine-Grained Control in Ranking-Based Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce the Edge Control Attack (ECA), the first fine-grained control attack tailored to ranking-based FL frameworks. |
Zhihao Chen; Zirui Gong; Jianting Ning; Yanjun Zhang; Leo Yu Zhang; |
| 155 | Generative Regression Based Watch Time Prediction for Short-Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by language modeling paradigms, we propose a novel Generative Regression (GR) framework that reformulates WTP as a sequence generation task. |
Hongxu Ma; Kai Tian; Tao Zhang; Xuefeng Zhang; Han Zhou; Chenghou Jin; Chunjie Chen; Han Li; Jihong Guan; Shuigeng Zhou; |
| 156 | Aligning Multiple Knowledge Graphs in A Single Pass Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Yaming Yang; Zhe Wang; Ziyu Guan; Wei Zhao; Weigang Lu; Xinyan Huang; Jiangtao Cui; Xiaofei He; |
| 157 | FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. |
Renqiang Luo; Huafei Huang; Tao Tang; Jing Ren; Ziqi Xu; Mingliang Hou; Enyan Dai; Feng Xia; |
| 158 | FairGU: Fairness-aware Graph Unlearning in Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, we observe that existing graph unlearning techniques insufficiently protect sensitive attributes, often leading to degraded algorithmic fairness compared with traditional graph learning methods. To address this gap, we introduce FairGU, a fairness-aware graph unlearning framework designed to preserve both utility and fairness during the unlearning process. |
Renqiang Luo; Yongshuai Yang; Huafei Huang; Qing Qing; Mingliang Hou; Ziqi Xu; Yi Yu; Jingjing Zhou; Feng Xia; |
| 159 | ONeRec: Towards Openness-Aware and Adaptive Proactive News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paradigm, however, presents three central challenges: (i) accurately modeling a user’s receptiveness to novelty; (ii) tracking evolving interests across multiple rounds of proactive recommendation; and (iii) selecting intermediary articles that balance immediate relevance with long-term target guidance. To tackle these challenges, we introduce ONeRec, a novel framework towards user Openness-aware and adaptive proactive News Recommendation. |
Jie Li; Zhen Cui; Linmei Hu; |
| 160 | Mamba Hawkes Process for Event Sequence Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce the Mamba Hawkes Process (MHP), the first framework to integrate selective state space model (Mamba) with temporal point processes. |
Shan Dai; Yuyang Shen; Yuyang Liang; Chenhao Ma; Anningzhe Gao; |
| 161 | Generative Archetype-Grounded Item Representations for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, the semantic space struggles to reflect actual user behavior, resulting in a significant gap between semantic representations and behavioral patterns. To address these limitations, we propose GenAIR, a general framework that empowers sequential recommendation with Generative Archetype-grounded Item Representations. |
Yifan Li; Jiahong Liu; Xinni Zhang; Hao Chen; Yankai Chen; Wenhao Yu; Jianting Chen; Irwin King; |
| 162 | Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present IntelGuard, a retrieval-augmented generation (RAG) based framework that integrates expert analytical reasoning into automated malicious package detection. |
Wenbo Guo; Shiwen Song; Jiaxun Guo; Zhengzi Xu; Chengwei Liu; Haoran Ou; Mengmeng Ge; Yang Liu; |
| 163 | Orion-Bix: Bi-Axial Attention for Tabular In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we introduce Orion-Bix, a tabular foundation model that combines biaxial attention with meta-learned in-context reasoning for few-shot tabular learning. |
Mohamed Bouadi; Pratinav Seth; Aditya Tanna; Vinay Kumar Sankarapu; |
| 164 | GPR: Empowering Generation with Graph-Pretrained Retriever Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. |
Xiaochen Wang; Zongyu Wu; Yuan Zhong; Xiang Zhang; Suhang Wang; Fenglong Ma; |
| 165 | Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing multi-agent frameworks often rely on static or task-level workflows, which either over-process simple queries or underperform on complex ones, while also neglecting the efficiency-performance trade-offs across heterogeneous LLMs. To address these limitations, we propose Difficulty-Aware Agentic Orchestration (DAAO), which can dynamically generate query-specific multi-agent workflows guided by predicted query difficulty. |
Jinwei Su; Qizhen Lan; Yinghui Xia; Lifan Sun; Weiyou Tian; Tianyu Shi; Lewei He; |
| 166 | FCRLLM: Aligning LLM with Collaborative Filtering for Long-tailed Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A key challenge, however, lies in effectively integrating semantic signals with collaborative representations, which originate from different modalities and learning dynamics. To tackle this, We propose a novel framework, called FCRLLM (the Flipped Classroom with LLM), for long-tail sequential recommendation that aligns collaborative and LLM-based semantic representations. |
Byungmoon Heo; Namjun Lee; Seonah Kim; Jaekwang Kim; |
| 167 | The Chatbot Knows It’s You: Dialogue Attribution in Unauthenticated Human–LLM Sessions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We challenge this assumption by formalizing Dialogue Attribution—the task of identifying the same user across disparate, unauthenticated human-LLM sessions, even under severe topic shifts. To rigorously quantify this threat, we introduce WildAuth, the first benchmark derived from real-world ChatGPT logs, and propose Uncertainty-aware Multi-aspect Attribution (UMA). |
Wenxuan Wang; Zirui Liu; Haoxuan Kou; Xuefeng Liu; Jiaxing Shen; |
| 168 | Enhancing Trusted Multi-View Classification Via Adaptive Regularization Guided By View-Specific Biases Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods typically enforce a uniform regularization objective across all views, overlooking critical view-specific biases: intra-view class ambiguity caused by confusable features and inter-view quality disparities reflected in evidence uncertainty. To address these issues, we propose an adaptive regularization strategy that enhances robustness on two levels. |
Zhiyuan Liu; Xiaodong Yue; Yufei Chen; Shijie Ding; Jie Shi; |
| 169 | StreamSense: Streaming Social Task Detection with Selective Vision–Language Model Routing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose StreamSense, a streaming detector that couples a lightweight streaming encoder with selective routing to a Vision–Language Model (VLM) expert. |
Han Wang; Deyi Ji; Lanyun Zhu; Jiebo Luo; Roy Ka-Wei Lee; |
| 170 | Distribution-Aligned Synthetic Text Generation Via Tail-Aware Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For instance, when models are iteratively trained on their own synthetic outputs, the upper tail of the perplexity distribution substantially compresses, with high-percentile values dropping by nearly half—a clear indicator of severe diversity loss. To counter this, we introduce DASGen, a Distribution-Aligned Synthetic Text Generation framework via tail-aware enhancement. |
Yuan Fan; Xiaoyuan Liu; Bo Liu; Wubing Wang; Jia Sun; Wenzhi Chen; Huaikang Fang; Lifeng Tao; Fan Mo; |
| 171 | Missingness-aware Federated Contrastive Learning on Semantic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present FedCL, a missingness-aware federated contrastive learning framework for dual-incomplete semantic graphs. |
Shuo Yu; Zhuoyang Han; Guoqing Han; Tao Tang; Feng Ding; Qiang Zhang; |
| 172 | Explaining Synergistic Effects in Social Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We quantify graph information gain to identify subgraphs embodying synergistic effects. Based on the theoretical insights, we propose SemExplainer, which explains synergistic effects by identifying subgraphs that embody them. |
Yicong Li; Shan Jin; Qi Liu; Shuo Wang; Jiaying Liu; Shuo Yu; Qiang Zhang; Kuanjiu Zhou; Feng Xia; |
| 173 | QChunker: Learning Question-Aware Text Chunking for Domain RAG Via Multi-Agent Debate Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, domain documents are characterized by dense terminology and strong contextual dependencies, which exacerbate the semantic fragmentation of text chunks, thereby making it difficult to efficiently utilize their key information. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. |
Jihao Zhao; Daixuan Li; Pengfei Li; Shuaishuai Zu; Biao Qin; Hongyan Liu; |
| 174 | E2SGNN: Reconciling Expression and Efficiency in Spiking Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To simultaneously reconcile considerable expression and efficiency, we propose E2SGNN, a novel network comprising a dual-scale modulated spiking backbone and a latency-dynamic optimization module. |
Han Zhao; Xu Yang; Cheng Deng; Fan Liu; |
| 175 | Unifying Diversity and Fairness in Re-ranking Via Economic Growth Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, directly optimizing this objective is NP-hard and requires global information, which poses significant computational challenges. To overcome these challenges, we propose a novel re-ranking algorithm named DivFair, which efficiently optimizes the objective function in online settings. |
Zhaofeng Li; Chen Xu; Xinyu Lin; Wenjie Wang; Xiaokui Xiao; |
| 176 | Multi-task Inference of Diffusion Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study how to infer multiple similar diffusion networks jointly with limited observation data for each network. |
Ting Gan; Kudereti Kuerban; Qian Yan; Ling Han; Zhigao Zheng; Hao Huang; |
| 177 | HCSL: Rumor Detection By Integrating Intra-Sample Curriculum Learning and Hierarchical Semantic Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose HCSL, a novel rumor detection framework that combines intra-sample curriculum learning (ISCL) and hierarchical semantic learning (HSL). |
Ping Liu; Hui Song; Shufeng Hao; Xiaoning Hao; Zexu Zhang; Usman Naseem; |
| 178 | HiFi-WF: Toward Realistic Website Fingerprinting with Multi-tab and Subpage Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose HiFi-WF (Hierarchical Fine-grained Website Fingerprinting), a novel framework that breaks the homepage-only assumption and extends WF to multi-tab recognition and fine-grained subpage identification. |
Songyang Wu; Chuan Ma; Ming Ding; Long Yuan; Biwen Chen; Yuwen Qian; Tao Xiang; |
| 179 | DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Importantly, these patterns evolve temporally within and across domains. Building on these insights, we propose a DGAD model with Dynamic Prototypes (DP) to capture evolving domain-specific and domain-agnostic patterns. |
Jialun Zheng; Jie Liu; Jiannong Cao; Xiao Wang; Hanchen Yang; Yankai Chen; |
| 180 | ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing long-term dialogue benchmarks primarily focus on static and explicit fact retrieval, failing to evaluate agents in these critical scenarios where user information is dispersed, implicit, and continuously evolving. To address this gap, we introduce ES-MemEval, a comprehensive benchmark that systematically evaluates five core memory capabilities—information extraction, temporal reasoning, conflict detection, abstention, and user modeling—in long-term emotional support scenarios, covering question answering, summarization, and dialogue generation tasks. |
Tiantian Chen; Jiaqi Lu; Ying Shen; Lin Zhang; |
| 181 | PLIKD: Prompt Learning with Instance-aware Knowledge Distillation for Web-scale Semantic Image Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This issue is especially challenging in Web-scale data, where new classes emerge and distributions shift dynamically. To address these limitations, we propose PLIKD, a novel prompt learning method that integrates instance-aware knowledge distillation for robust Web-scale semantic image classification. |
Jianye Xie; Chunhua Hu; Lianyong Qi; Fan Wang; Xiaolong Xu; Haolong Xiang; Xuyun Zhang; Shichao Pei; Amin Beheshti; Wanchun Dou; Xiaokang Zhou; |
| 182 | Kardia-R1: Unleashing LLMs to Reason Toward Understanding and Empathy for Emotional Support Via Rubric-as-Judge Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This progressive empathy pipeline that integrates user comprehension, contextual reasoning, and emotion perception into conversations, followed by iterative critique and rubric-based refinement to ensure psychological plausibility, emotional fidelity, and persona consistency. Building on this, we propose Kardia-R1, a framework that trains models for interpretable, stepwise empathetic cognition. |
Jiahao Yuan; Zhiqing Cui; Hanqing Wang; Yuansheng Gao; Yucheng Zhou; Usman Naseem; |
| 183 | Rethink Web Service Resilience in Space: A Radiation-Aware and Sustainable Transmission Solution Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose RALT (Radiation-Aware LEO Transmission), a control-plane solution that dynamically reroutes traffic during radiation events, accounting for energy constraints to minimize battery degradation and sustain service performance. |
Long Chen; Hao Fang; Yi Ching Chou; Haoyuan Zhao; Xiaoyi Fan; Zhe Chen; Hengzhi Wang; Jiangchuan Liu; |
| 184 | ThinkRec: Thinking-based Recommendation Via LLM Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: This often leads to superficial and erroneous recommendations. Inspired by this, we propose ThinkRec, a thinking-based framework that shifts LLM4Rec from an intuitive system to a rational system. |
Qihang Yu; Kairui Fu; Zheqi Lv; Shengyu Zhang; Xinhui Wu; Chen Lin; Feng Wei; Bo Zheng; Fei Wu; |
| 185 | Unlocking The Multilingual Long-Tail Web: A Fused Macro-Micro Framework for Scalable Content Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce the Control-Tower Framework (CTF), a novel methodology designed to systematically enhance powerful, pre-trained base models. |
Jiarui Zhang; Yifan Deng; Qihao Wang; |
| 186 | Does This Button Work? Investigating YouTube’s Ineffective User Controls Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a large-scale experimental audit of YouTube’s user control mechanisms for managing unwanted video recommendations. |
Jesse McCrosky; Ranadheer Malla; Aapo Tanskanen; Chico Camargo; |
| 187 | DOS: Dual-Flow Orthogonal Semantic IDs for Recommendation in Meituan Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods suffer from two major limitations: (1) the lack of contextual awareness in generation tasks leads to a gap between the Semantic ID codebook space and the generation space, resulting in suboptimal recommendations; and (2) suboptimal quantization methods exacerbate semantic loss in LLMs. To address these issues, we propose Dual-Flow Orthogonal Semantic IDs (DOS) method. |
Junwei Yin; Senjie Kou; Changhao Li; Shuli Wang; Yinqiu Huang; Xue Wei; Yinhua Zhu; Haitao Wang; Xingxing Wang; |
| 188 | Does LLM Focus on The Right Words? Mitigating Context Bias in LLM-based Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This bias not only undermines recommendation accuracy but also raises unfairness concerns. To address this issue, we propose Group Distributionally Robust Optimization-based Tuning (GDRT), a novel fine-tuning paradigm that enforces consistent model performance across token groups with varying degrees of relevance to auxiliary tokens. |
Bohao Wang; Jiawei Chen; Feng Liu; Changwang Zhang; Jun Wang; Canghong Jin; Chun Chen; Can Wang; |
| 189 | TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current evaluation faces two primary challenges: 1) a reliance on singular metrics like Pass@1, creating a ”high-score illusion” that ignores the quality, efficiency, and soundness of the reasoning process; and 2) the failure of static benchmarks to quantify crucial attributes like robustness and latent capability. To address these gaps, we introduce TRACE (Trajectory-Aware Comprehensive Evaluation), a framework that holistically assesses the entire problem-solving trajectory. |
Yanyu Chen; Jiyue Jiang; Jiahong Liu; Yifei Zhang; Xiao Guo; Irwin King; |
| 190 | ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, when applied to time series data, they face inherent limitations due to context length. To address this issue, we propose ViTs, a Vision-Language Model (VLM)-based framework that converts time series curves into visual representations. |
Zexin Wang; Changhua Pei; Yang Liu; Hengyue Jiang; Quan Zhou; Haotian Si; Hang Cui; Jianhui Li; Gaogang Xie; Jingjing Li; Dan Pei; |
| 191 | STaR: Towards Effective and Stable Table Reasoning Via Slow-Thinking Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose STaR, a novel slow-thinking model that can achieve effective and stable table reasoning. |
Huajian Zhang; Mingyue Cheng; Yucong Luo; Xiaoyu Tao; |
| 192 | Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a manipulation-resistant copy-trading system based on a multi-agent architecture powered by a multi-modal large language model (LLM) and chain-of-thought (CoT) reasoning. |
Yichen Luo; Yebo Feng; Jiahua Xu; Yang Liu; |
| 193 | Knowledge-Enhanced Multimodal Fake News Detection: Semantic Visual and Priority Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the issues, this paper proposes a multimodal fake news detection method, SVPF-Net, that centers on semantic-driven visual enhancement and knowledge-aided modality-priority fusion. |
Qin Zhang; Jiaying Liu; Qian Tao; Zhiwei Guo; Qiyue Zhong; Yifan Zhang; Ziyan Huang; |
| 194 | GIANT: Structure-Agnostic Practical Adversarial Attacks for Graph-based Network Intrusion Detection Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The lack of practical robustness evaluation severely hinders the deployment of GNIDS in real-world security applications. To fill this gap, we propose GIANT, a structure-agnostic practical adversarial attack framework for comprehensive robustness evaluation of GNIDS. |
Jianjin Zhao; Dongqi Han; Chao Ma; Qi Li; Zhiwei Cui; Hongliang Zhu; Hua Zhang; Mingshu He; Yijun Lu; Jiong Dong; Yuyin Ma; Meng Shen; |
| 195 | S2CDR: Smoothing-Sharpening Process Model for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel paradigm of Smoothing-Sharpening Process Model for CDR to cold-start users, termed as S2CDR which features a corruption-recovery architecture and is solved with respect to ordinary differential equations (ODEs). |
Xiaodong Li; Juwei Yue; Xinghua Zhang; Jiawei Sheng; Wenyuan Zhang; Taoyu Su; Zefeng Zhang; Tingwen Liu; |
| 196 | LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: 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. |
Hao Liang; Qifeng Cai; Zhaoyang Han; Hejun Dong; Meiyi Qiang; Ruichuan An; Quanqing Xu; Bin Cui; Wentao Zhang; |
| 197 | MCRec: Few-Shot Multimodal Cover Recommendation Via User Interest Profiles Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address cold-start and sparsity challenges in traditional methods, we propose Multimodal Cover Recommendation (MCRec), a framework that leverages Vision-Language Models (VLMs) for multimodal feature extraction. |
Weixin Zheng; Chunyao Song; Tingjian Ge; |
| 198 | Counterfactual Augmented Causal Reasoning for Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a new counterfactual-enhanced causal reasoning (CECR) framework to reduce spurious correlations. |
Yiming Wu; Liang Hu; Mingzhu Zhou; Tangwei Ye; Xuejie Yang; Xun Yang; Zhongyuan Lai; Qi Zhang; Usman Naseem; |
| 199 | Glasses: Enabling Fast Environment-aware Few-Shot Learning Via Device-Cloud Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, FSL often suffers from poor performance due to its inability to adapt to the characteristics of the deployment environments, while backbone fine-tuning prior to model deployment is typically infeasible because of the unavailability of environment-specific samples. To tackle this challenge, this paper presents Glasses, a lightweight fine-tuning scheme that can adapt ViT-based model backbones to deployment environments rapidly through device-cloud collaboration, helping the model achieve better FSL performance on the device. |
Qiang He; Sheng Zhong; Jiazhen Yang; Feifei Chen; Hai Jin; Yun Yang; |
| 200 | Thorki: Decoupling General and Personalized Knowledge with Collaborative Fusion for Personalized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents Thorki, a new FL system that decouples general and personalized knowledge throughout all model layers. |
Qiang He; Rui Chen; Yicheng Liu; Sheng Zhong; Haipeng Dai; Feifei Chen; Hai Jin; Yun Yang; |
| 201 | What Should I Cite? A RAG Benchmark for Academic Citation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (3) We propose a multi-level hybrid RAG approach to citation prediction, fine-tuning embedding models with contrastive learning to capture complex citation relationships, paired with specialized generation models. |
Leqi Zheng; Jiajun Zhang; Canzhi Chen; Chaokun Wang; Hongwei Li; Yuying Li; Yaoxin Mao; Shannan Yan; Zixin Song; Zhiyuan Feng; Zhaolu Kang; Zirong Chen; Hang Zhang; Qiang Liu; Liang Wang; Ziyang Liu; |
| 202 | Unlearning of Knowledge Graph Embedding Via Preference Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (2) It focuses on local data for specific removal, which weakens the remaining knowledge in the forgetting boundary. To address these issues, we propose GraphDPO, a novel approximate unlearning framework based on direct preference optimization (DPO). |
Jiajun Liu; Wenjun Ke; Peng Wang; Yao He; Ziyu Shang; Guozheng Li; Zijie Xu; Ke Ji; |
| 203 | Truth with A Twist: The Rhetoric of Persuasion in Professional Vs. Community-Authored Fact-Checks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study presents the first large-scale comparison of persuasion techniques present in crowd- versus professionally-written debunks. |
Olesya Razuvayevskaya; Kalina Bontcheva; |
| 204 | FUSED: Toward Federated Multimodal Retrieval Across Sovereign Data Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These sovereign data domains often restrict raw data aggregation and the disclosure of model parameters or embeddings, making cross-site retrieval difficult to achieve with existing centralized or federated approaches. We present FUSED, a sovereignty-preserving multimodal retrieval framework to address these challenges. |
Chi Xu; Jiaxing Li; Mengdi Jin; William I. Atlas; Mark A. Spoljaric; Edith C.H. Ngai; Jiangchuan Liu; |
| 205 | ARuleCon: Agentic Security Rule Conversion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose ARuleCon, an agentic SIEM-rule conversion approach. |
Ming Xu; Hongtai Wang; Yanpei Guo; Zhengmin Yu; Weili Han; Hoon Wei Lim; Jin Song Dong; Jiaheng Zhang; |
| 206 | LongRanker: Efficient One-Pass Document Reranking with Long-Context Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a long-context listwise document reranker, LongRanker, and make two major contributions to enable long-context LLMs for listwise reranking: (i) To improve length extrapolation for listwise inputs, we introduce an intra-inter hierarchical positional encoding approach that combines intra-document encoding to identify token locations within a document with inter-document encoding to specify the document index. |
Changjiang Zhou; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Fan Yixing; Xueqi Cheng; |
| 207 | Riemannian Liquid Spatio-Temporal Graph Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This limitation introduces significant geometric distortion when representing real-world graphs with inherent non-Euclidean structures (e.g., hierarchies and cycles), degrading representation quality. To overcome this limitation, we introduce the Riemannian Liquid Spatio-Temporal Graph Network (RLSTG), a framework that unifies continuous-time liquid dynamics with the geometric inductive biases of Riemannian manifolds. |
Liangsi Lu; Jingchao Wang; Zhaorong Dai; Hanqian Liu; Yang Shi; |
| 208 | Rhythm of Opinion: Interpretable Hawkes-Graph Networks for Hierarchical Opinion Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these limitations, we introduce VISTA, a multi-dimensional opinion propagation dataset providing complete hierarchical structures, fine-grained emotional annotations, and cross-domain coverage. Based on this dataset, we propose an interpretable modeling framework integrating high-dimensional Hawkes processes with graph neural networks, enabling parametric expression of propagation mechanisms through event space constructed from emotional and reply level combinations. |
Yulong Li; Zhixiang Lu; Peixin Guo; Simin Lai; Yuxuan Zhang; Haochen Xue; Xiwei Liu; Yichen Li; Zhaodong Wu; Feilong Tang; Mian Zhou; Chong Li; Imran Razzak; Qingxia Li; Jionglong Su; |
| 209 | Multi-Source Information Driven Spatio-Temporal Hypergraph Learning for Traffic Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although recent graph-based deep learning methods have achieved promising results, most focus on pairwise neighbor relationships, limiting their ability to capture higher-order spatio-temporal interactions in the traffic network. To overcome this limitation, we propose a novel Multi-source information driven Spatio-Temporal HyperGraph learning for traffic forecasting (MSTHG), which is designed to capture richer relational and semantic information. |
Ping Zhang; Jiayu Leng; Liang Yang; Anchen Li; Xiaochun Cao; Riting Xia; |
| 210 | Adaptive Multi-Interaction Web Semantic Graph Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, these semantic characteristics across different relationships cannot be easily captured by a simple linear combination. To tackle this challenge, we propose an Adaptive Multi-Interaction (AMI) web semantic graph representation method. |
Feng Ding; Tingting Wang; Ruolin Li; Ying Jin; Junxiang Zhang; Shan Jin; Yicong Li; Xin Ye; |
| 211 | KE-FedRS: Tackling Data Sparsity in Federated Recommendation Via Knowledge Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Considering these, we propose the Knowledge Enhanced Federated Recommendation System named as KE-FedRS, of which the core idea is to enhance the knowledge of users with few interactions and items with few ratings at both the local and global levels. Specifically, at the local level, we introduce an auxiliary user embedding and average and aggregate this auxiliary embedding across similar users, thereby enriching the knowledge of the local user embedding. |
Jiayu Bao; Hongjian Shi; Guanyu Zhang; Rui Zhou; Haozhao Wang; Yuan Liu; |
| 212 | Automated Deterministic Auction Design with Objective Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing research primarily focuses on randomized auctions, with less attention given to more practical deterministic auctions. Therefore, in this paper, we introduce OD-VVCA, an objective decomposition approach for automated designing revenue-maximizing deterministic Virtual Valuations Combinatorial Auctions (VVCAs), which are inherently DSIC and IR. |
Zhijian Duan; Haoran Sun; Yichong Xia; Siqiang Wang; Zhilin Zhang; Chuan Yu; Jian Xu; Xiaotie Deng; |
| 213 | OMGRec: One-time Matching-based Generative Rerank with Permutation-level Modeling in E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing approaches either rely on point-wise scoring, which overlooks permutation-level dependencies, or adopt autoregressive paradigms that enhance context modeling at the cost of prohibitively high inference latency in practical applications. To address these limitations, we present OMGRec, a one-time matching-based generative reranking method. |
Junwei Xu; Zhibo Xiao; Chuxin Chen; Chengyu Lai; Qijie Shen; Jiuning Lin; Dimin Wang; Jialin Zhu; Xiao-Ping Zhang; |
| 214 | Room Matters: Dynamic Room-level Collaboration Information Modeling for Live Streaming Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing studies have not thoroughly investigated the dynamics of room-level collaborative information. In this paper, we address this gap by emphasizing two perspectives: the evolving tripartite interaction information among rooms, streamers, and users, and the real-time intra-room collaboration information. |
Ke Guo; Changle Qu; Xiao Zhang; Liqin Zhao; Shijun Wang; Yanan Niu; Jun Xu; |
| 215 | Beyond The Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel framework named HPGR (Hierarchical and Preference-aware Generative Recommender), built upon a two-stage paradigm that injects these crucial structural priors into the model to handle the drawback. |
Zerui Chen; Heng Chang; Tianying Liu; Chuantian Zhou; Yi Cao; Jiandong Ding; Ming Liu; Bing Qin; |
| 216 | LPEdit: Locality-Preserving Knowledge Editing for MultiModal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Here, we propose LPEdit, a novel method that leverages the null space projection on key layers to focus editing on conveyed visual information without influencing unrelated knowledge. |
Tianyu Zhang; Junfeng Fang; Houcheng Jiang; Xingyu Zhu; Xiang Wang; Xiangnan He; |
| 217 | Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions, such as repeated purchases, and how different levels of contextual information shape their predictive behavior. |
Yanan Cao; Farnaz Fallahi; Murali Mohana Krishna Dandu; Lalitesh Morishetti; Kai Zhao; Luyi Ma; Sinduja Subramaniam; Jianpeng Xu; Evren Korpeoglu; Kaushiki Nag; Sushant Kumar; Kannan Achan; |
| 218 | XR: Cross-Modal Agents for Composed Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While embedding-based CIR methods have achieved progress, they remain narrow in perspective, capturing limited cross-modal cues and lacking semantic reasoning. To address these limitations, we introduce XR, a training-free multi-agent framework that reframes retrieval as a progressively coordinated reasoning process. |
Zhongyu Yang; Wei Pang; Yingfang Yuan; |
| 219 | Towards Robust Heterogeneous Graph Explanations Under Structural Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, real-world graphs are often heterogeneous and subject to structural noise, posing severe challenges to the robustness of existing explanation methods. To address these issues, we propose RoHeX, a Robust Heterogeneous GNN Explainer that enhances explanation quality under noisy conditions. |
Yifan Lu; Pengfei Jiao; Xuan Guo; Ziyun Zou; Yiwei Wang; Mengzhou Gao; Huaming Wu; Imran Razzak; |
| 220 | DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose DyMRL, a Dynamic Multispace Representation Learning approach to efficiently acquire and fuse multimodal temporal knowledge. |
Feng Zhao; Kangzheng Liu; Teng Peng; Yu Yang; Guandong Xu; |
| 221 | BalDRO: A Distributionally Robust Optimization Based Framework for Large Language Model Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A key challenge in LLM unlearning lies in the sample-wise imbalance within the forget set: different samples exhibit widely varying unlearning difficulty, leading to asynchronous forgetting speeds where some knowledge remains insufficiently erased while others become over-forgotten. To address this challenge, we propose BalDRO, a novel and efficient framework for balanced LLM unlearning. |
Pengyang Shao; Naixin Zhai; Lei Chen; Yonghui Yang; Fengbin Zhu; Xun Yang; Meng Wang; |
| 222 | VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These limitations make it difficult to achieve favorable trade-offs across diverse human values. To address these challenges, we revisit multi-value alignment from the perspective of value consistency in data and propose VC-soup, a data filtering and parameter merging framework grounded in value-consistent learning. |
Hefei Xu; Le Wu; Yu Wang; Min Hou; Han Wu; Zhen Zhang; Meng Wang; |
| 223 | Graph Diffusion Evolution Model for Multi-Conditional Molecular Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the paradigm of directly generating new molecules from conditions used in existing work has not accurately fitted the joint distribution of multiple conditions during the generation process. To address this issue, we propose Graph Diffusion Evolution Model(GDEM) for multi conditional molecule generation. |
Xingcheng Fu; Lingyun Liu; Yisen Gao; Tianyu Chen; Qingyun Sun; Jianxin Li; Xianxian Li; |
| 224 | SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing CDR methods often rely on domain-specific features or identifiers that lack transferability across different domains, limiting their ability to capture inter-domain semantic patterns. To overcome this, we propose model, a semantics-driven framework for cross-domain sequential recommendation that leverages large language models (LLMs) to construct a unified semantic space. |
Chunxu Zhang; Shanqiang Huang; Zijian Zhang; Jiahong Liu; Linsong Yu; Ruiqi Wan; Bo Yang; Irwin King; |
| 225 | Breath: Adaptive Protection Boundary in FEC Encoding for Mobile Real-Time Video Streaming Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Breath, an adaptive FEC scheme that dynamically adjusts the protection boundary based on network and video dynamics. |
Shiyang Huang; Gerui Lv; Yuankang Zhao; Jiaxing Zhang; Qingyue Tan; Congkai An; Huanhuan Zhang; Xinyi Zhang; Qinghua Wu; Zhenyu Li; |
| 226 | Facilitating Generative Retrieval with Logical Denoising for Interpretable Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose LogiCGR, a novel framework that utilizes curriculum learning and group relative policy optimization (GRPO) to perform logic-enhanced retrieval, improving the robustness and interpretability of conversational search. |
Qichuan Liu; Chentao Zhang; Yuxuan Hu; Chenfeng Zheng; Qinggang Zhang; Zhihong Zhang; |
| 227 | Augmenting Cross-View Geo-Localization with Spatial Semantics from Vision Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We reformulate CVGL from a spatial perspective and propose an auxiliary task-enhanced network. |
Ji Shen; Lixing Chen; Yang Bai; Zhongqi Miao; Zhe Qu; Pan Zhou; Jianhua Li; |
| 228 | SentinelNet: Safeguarding Multi-Agent Collaboration Through Credit-Based Dynamic Threat Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing defenses often fall short due to reactive designs or centralized architectures which may introduce single points of failure. To address these challenges, we propose SentinelNet, the first decentralized framework for proactively detecting and mitigating malicious behaviors in multi-agent collaboration. |
Yang Feng; Xudong Pan; |
| 229 | Thinking Bidirectionally: A Reasoning and Self-Correction Approach for Text-Based Event Prediction with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) exhibit potential in processing and understanding text, current text-based event prediction faces two primary challenges: first, an insufficient utilization of potential information within the text, such as causal relationships and latent associations, and second, limited predictive reliability constrained by issues like the LLM’s own ability and hallucinations. To address these challenges, we propose a novel event prediction framework, Bidirectional Reasoning with Self-Correction (BRSC). |
Liwei Qian; Hang Zhang; Yiheng Wu; Yanmin Li; Mengna Zhu; Lihua Liu; Jibing Wu; |
| 230 | UrbanMoE: A Sparse Multi-Modal Mixture-of-Experts Framework for Multi-Task Urban Region Profiling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Concurrently, we propose UrbanMoE, the first sparse multi-modal, multi-expert framework specifically architected to solve the multi-task challenge. |
Pingping Liu; Jiamiao Liu; Zijian Zhang; Hao Miao; Qi Jiang; Qingliang Li; Qiuzhan Zhou; Irwin King; |
| 231 | FairFS: Addressing Deep Feature Selection Biases for Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We refer to these biases as layer bias, baseline bias, and approximation bias. To mitigate these biases, we propose FairFS, a fair and accurate feature selection algorithm. |
Xianquan Wang; Zhaocheng Du; Jieming Zhu; Qinglin Jia; Zhenhua Dong; Kai Zhang; |
| 232 | Topology-Aware Feature Sorting Enables Universal Modeling on Homophilic and Heterophilic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This finding enables GFMs to adapt to heterophilic graphs and better utilize the small amount of heterophilic information in homophilic graphs. Based on this, we propose Topology-aware Feature Sorting Graph Foundation Model (TFSGFM), which employs a feature-level topology-aware sorting strategy and a dual-channel graph neural network framework, enabling unified modeling of both feature and structure. |
Yi Wang; Jitao Zhao; Dongxiao He; Jia Li; Yuxiao Huang; Zhiyong Feng; |
| 233 | Platform Competition in The Autobidding World Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on advertisers with return-over-investment (henceforth, ROI) constraints, i.e. each advertiser is trying to maximize value while making sure that their ROI across all platforms is no less than some target value. |
Gagan Aggarwal; Andres Perlroth; Ariel Schvartzman; Mingfei Zhao; |
| 234 | Online Advertising with Spatial Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such spatial externalities — arising from proximity, clutter, or crowding — can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce a new framework for spatial externalities in online advertising, in which the value of an ad depends on both its slot and the configuration of surrounding ads. |
Gagan Aggarwal; Yifan Wang; Mingfei Zhao; |
| 235 | Unbiased Multimodal Reranking for Long-Tail Short-Video Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a LLM-driven multimodal reranking framework, which estimates user experience without real user behavior. |
Wenyi Xu; Feiran Zhu; Songyang Li; Renzhe Zhou; Chao Zhang; ChengLei Dai; Yuren Mao; Yunjun Gao; Yi Zhang; |
| 236 | Exploring Fine-Tuning for Tabular Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work presents the first comprehensive study of fine-tuning in TFMs across benchmarks including TALENT, OpenML-CC18, and TabZilla. |
Aditya Tanna; Pratinav Seth; Mohamed Bouadi; Vinay Kumar Sankarapu; |
| 237 | From Modularity to Unity: Towards Industrial-Scale Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Generative recommendation represents a promising path from modularity to unity by consolidating these architectures within a single framework, yet still faces critical challenges: an incompatibility between generative architectures and discriminative tasks, gradient interference across objectives in a shared backbone, and misaligned heterogeneous sequences. To address these challenges, we present UniGenR, a unified ranking framework that fundamentally rethinks generative recommendation models (GRM) design. |
Xiaofeng Liu; Wenliang Li; Tao Yu; Guanliang Song; Zhen Chen; Jing Zhou; Dongyue Wang; Xiwei Zhao; Sulong Xu; |
| 238 | D-Models and E-Models: Diversity-Stability Trade-offs in The Sampling Behavior of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: The predictive probability of the next token (P_token) in large language models (LLMs) is inextricably linked to the probability of relevance for the next piece of information, … |
Jia Gu; Liang Pang; Huawei Shen; Xueqi Cheng; |
| 239 | StreamFP: Fingerprint-guided Data Selection for Efficient Stream Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose StreamFP, a lightweight SL framework that introduces fingerprints-a set of compact, learnable parameter vectors that summarize the model state. |
Changwu Li; Tongjun Shi; Shuhao Zhang; Binbin Chen; Bingsheng He; Xiaofei Liao; Hai Jin; |
| 240 | Hierarchical Graph-Bag-Network for Self-Supervised Multi-Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While self-supervised contrastive learning offers a compelling solution, its direct application to MGL faces three key challenges: (1) existing graph neural networks, primarily for single-graph modeling, struggle to yield discriminative bag-level representations from bags-of-graphs; (2) conventional contrastive objectives are limited to single-level settings, failing to capture cross-hierarchical dependencies; and (3) standard data augmentation often disrupts intrinsic graph and bag structures, undermining semantic consistency. To address these issues, we propose the Hierarchical Graph-Bag-Network (HGBN), a self-supervised MGL framework that constructs hierarchical representations in the form of a graph-bag-network. |
Meixia Wang; Yuhai Zhao; Zhengkui Wang; Fenglong Ma; Yejiang Wang; Miaomiao Huang; Fazal Wahab; Wen Shan; Xingwei Wang; |
| 241 | BeeQoS: A Cloud-Native QoS System for Adaptive and Scalable Multi-Priority Bandwidth Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present BeeQoS, a cloud-native QoS system that delivers low-latency, adaptive, and scalable multi-priority bandwidth guarantees. |
Jinyao Liu; Si Wu; Haoyuan Ma; Chaoqun Li; Hongjing Yu; Dingyi Jia; Feng Li; Pengfei Hu; |
| 242 | Fate: Fasss SEsdge Inference of Mixture-of-Experts Models Via Cross-Layer Gate Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To promote the application of MoE models in edge scenarios, we propose Fate, an offloading system designed for MoE models to enable efficient inference in resource-constrained environments. |
Zhiyuan Fang; Xingfan Yu; Yuegui Huang; Zicong Hong; Yufeng Lyu; Wuhui Chen; Yue Yu; Fan Yu; |
| 243 | Semi-Supervised Fake News Detection with Mixture of Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a Semi-supervised Mixture of Experts framework for Fake news detection, namely S2MOE-F. |
Zhenyu Yang; Chaoyu Yang; Wenfeng Xu; Xiuxiu Hao; Huan Wang; Ge Zhang; Xiaoxiao Ma; Jun Shen; |
| 244 | Hyena Operator for Fast Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We argue that Hyena faces challenges in recommendation due to limited representation capacity on sparse, long user sequences. To address these challenges, we propose HyenaRec, a novel sequential recommender that integrates polynomial-based kernel parameterization with gated convolutions. |
Jiahao Liu; Lin Li; Zhiyuan Li; Kaixi Hu; Kaize Shi; Jingling Yuan; |
| 245 | FalconScope: Effective and Efficient Detection of Hidden Web Interfaces in IoT Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite recent advancements, current detection approaches suffer from two critical challenges: 1) inadequately model the complex internal routing mechanisms of IoT firmware, leading to incomplete interface enumeration and substantial false negatives; and 2) inefficiently generate probing requests and verify unauthorized access due to limited semantic understanding of interface communication protocols. To overcome these challenges, we introduce FalconScope, a novel system combining precise firmware routing modeling and Large Language Model (LLM)-driven semantic analysis to detect hidden web interfaces effectively and efficiently. |
Jiaming Guo; Haoran Yang; Kuihao Yan; Jiekang Hu; Xiaoqi Jia; Haichao Du; Qihang Zhou; |
| 246 | Cardinality Is Not Enough: Super Host Detection Via Segmented Cardinality Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Though hierarchical-structure based approaches could capture flow cardinality in subnet, they inherently suffer from high memory usage. To address these limitations, we propose SegSketch, a segmented cardinality estimation approach that employs a lightweight halved-segment hashing strategy to infer common prefix lengths of IP addresses, and estimates cardinality within subnet to enhance detection accuracy under constrained memory size. |
Yilin Zhao; Jiawei Huang; Xianshi Su; Weihe Li; Xin Li; Yan Liu; Jiacheng Xie; Qichen Su; Jin Ye; Wanchun Jiang; Jianxin Wang; |
| 247 | Vertical Semi-Federated Learning for Efficient Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-world industrial applications, where the learned model retains sufficient advantages of federated learning while supporting independent local serving. To achieve this goal, we propose the carefully designed Joint Privileged Learning framework (JPL ) to i) alleviate the absence of the passive party’s feature with federated equivalence imitation and ii) adapt to the heterogeneous full sample space with cross-branch rank alignment. |
Wenjie Li; Shu-Tao Xia; Jiangke Fan; Teng Zhang; Xingxing Wang; |
| 248 | Generalizable Graph-level Anomaly Detection Via Prompted Anomaly Expansion and Normality Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Detecting them is highly challenging due to two key issues: (i) labeled anomalous graphs are extremely limited and fail to capture the diversity of real-world abnormality, restricting detection models from generalizing to unseen anomalies encountered in the open world, and (ii) normal graphs often contain spurious or atypical substructures that do not indicate anomalies but may induce models to misclassify normal variations as anomalies. To tackle these challenges, we propose G-GLAD, a generalizable graph-level anomaly detection framework. |
Ge Zhang; Jiapei Chen; Guohao Sun; Xiu Fang; Zhenyu Yang; Xixun Lin; Liang Yang; |
| 249 | SQL-Checker: Error Detection and Labeling for Text-to-SQL with Interpretability Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing SQL error detection methods are costly, lack interpretability, and do not support error labeling. To overcome these issues, we propose SQL-Checker a specialized model for Text-to-SQL error detection. |
Xingyu Ma; Xin Tian; Lingxiang Wu; Xuepeng Wang; Xueming Tang; Jinqiao Wang; |
| 250 | DualGR: Generative Retrieval with Long and Short-Term Interests Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, deploying GR in short-video feeds remains challenged by long-short interest interference, context-induced noise in hierarchical SID generation, and the lack of explicit learning from exposed-but-unclicked feedback. To address these challenges, we propose DualGR, which combines (i) a Dual-Branch Long/Short-Term Router (DBR) with selective activation, (ii) Search-based SID Decoding (S2D) that constrains fine-level decoding within the current coarse bucket for efficiency and noise control, and (iii) an Exposure-aware Next-Token Prediction Loss (ENTP-Loss) that treats unclicked exposures as coarse-level hard negatives to promote timely interest fade-out. |
Zhongchao Yi; Kai Feng; Xiaojian Ma; Yalong Wang; Yongqi Liu; Han Li; Zhengyang Zhou; Yang Wang; |
| 251 | Conditional Information Extraction with Diffusion Model on Fact-Condition Star Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a Diffusion Model on Fact-condition Star Graph for CIE (Diff-CIE). |
Yunxiao Yang; Jianting Chen; Xiaoying Gao; Zaiyuan Di; Yang Xiang; |
| 252 | CausalSKyHop: Knowledge-Aware Causal Explanation of Dynamic GNNs Via Higher-Order Semantic Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CausalSKyHop, a semantic- and knowledge-aware framework that explains DyGNNs by uncovering causal higher-order patterns in evolving knowledge structures. |
Jixuan Wu; Limei Lin; Xiaoding Wang; Kunpeng Xu; Jie Wu; |
| 253 | RegimeGuard: Continual Learning Queue Scheduling for Socially Critical Web Services Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present RegimeGuard, a continual-learning framework for queue scheduling on programmable switches. |
Zhipeng Liu; Ziming Zhao; Fan Zhang; |
| 254 | A Unified Framework for Rule Learning: Integrating Commonsense Knowledge from LLMs with Structured Knowledge from Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To leverage the strengths of both approaches, we propose a unified framework CSRL to integrate the commonsense knowledge of LLMs with the structured knowledge from KGs for logical rule learning. |
Qirui Hao; Kewei Cheng; Tongze Zhang; Hongyuan Liu; Junming Shao; Carl Yang; |
| 255 | CCL-Diff: Representation-Consistent Diffusion with Intrinsic Contrastive Learning for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they often overlook an essential aspect: the semantic consistency of representations across diffusion timesteps, limiting their robustness and generalizability. To address this, we propose CCL-Diff, a novel diffusion-based recommendation framework enhanced by Cross-Denoising Contrastive Learning (CCL). |
Lingyan Zhang; Wanyu Ling; Shuwen Daizhou; Li Kuang; Kehua Guo; |
| 256 | Inferential Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Inferential QA–a new task that challenges models to infer answers from answer-supporting passages which provide only clues. |
Jamshid Mozafari; Hamed Zamani; Guido Zuccon; Adam Jatowt; |
| 257 | VarParser: Unleashing The Neglected Power of Variables for LLM-based Log Parsing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Third, a relatively large number of consumed constant tokens in prompts leads to high LLM invocation costs. At last, these methods only retain placeholders in the results, losing the system visibility brought by variable information in logs.Facing these problems, we propose a variable-centric log parsing strategy named VarParser. |
Jinrui Sun; Tong Jia; Minghua He; Ying Li; |
| 258 | AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The absence of standardized datasets and evaluation protocols prevents fair and reproducible assessment of documentation quality. To address these challenges, we propose AdaQE-CG, an Adaptive Query Expansion for Card Generation framework that integrates dynamic information extraction with cross-card knowledge transfer. |
Haoxuan Zhang; Ruochi Li; Zhenni Liang; Mehri Sattari; Phat Vo; Collin Qu; Ting Xiao; Junhua Ding; Yang Zhang; Haihua Chen; |
| 259 | DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we ask: ”Can neural architectures discover social exchange principles from behavioral data alone?” |
Lin Tian; Marian-Andrei Rizoiu; |
| 260 | Hermes The Polyglot: A Unified Framework to Enhance Expressiveness for Multimodal Interlingual Subtitling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although Large Language Models (LLMs) have significantly advanced the general capabilities of machine translation, the distinctive characteristics of subtitle texts pose persistent challenges in interlingual subtitling, particularly regarding semantic coherence, pronoun and terminology translation, and translation expressiveness. To address these issues, we present Hermes, an LLM-based automated subtitling framework. |
Chaoqun Cui; Shijing Wang; Liangbin Huang; Qingqing Gu; Zhaolong Huang; Xiao Zeng; Wenji Mao; |
| 261 | ONE-PASS: Single Forward Pass Decoding for Listwise Reranking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a Single Forward Pass (SFP)-based method to pre-verify multiple rankings using tree attention, approximating auto-regressive decoding by relevant sub-rankings at each step. |
Yingpeng Du; Zhu Sun; Tianjun Wei; Jie Zhang; |
| 262 | The Query Complexity of Uniform Pricing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet in the multi-distribution case, can the regularity and MHR conditions still lead to improvements over the tight bound ~Θ (ε-3) for general distributions? We answer this question in the negative, by establishing a (near-)matching lower bound \O{}mega(ε-3) for either two regular distributions or three MHR distributions.We also address the regret minimization problem and, in comparison with the folklore upper bound ~O(T2/3 ) for general distributions (see, e.g., [13]), establish a (near-)matching lower bound \O{}mega(T2/3 ) for either two regular distributions or three MHR distributions, via a black-box reduction. |
Houshuang Chen; Yaonan Jin; Pinyan Lu; Chihao Zhang; |
| 263 | CEAT: Context-Emotion Adversarial Training Framework for Robust Emotion-Driven Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, prevalent evaluations are typically confined to unimodal perturbations and fail to account for context-consistent, cross-modal attacks, thereby compromising system reliability in real-world deployments. To bridge this gap, we propose Context-Emotion Adversarial Training (CEAT), a robust framework designed to fortify multimodal fraud detection against emotion-based attacks. |
Chaoqun Li; Si Wu; Yijun Lu; Yuyin Ma; Jinyao Liu; Dingyi Jia; Mingda Han; Feng Li; Pengfei Hu; |
| 264 | A Fact-Checking Framework with Denoising Evidence Retrieval and LLM-Based Debate Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these methods face two major challenges: (1) the retrieval process often introduces noisy evidence, which compromises the reliability of the final veracity prediction; and (2) the verification models may overlook critical factual details, resulting in hallucinated conclusions. To address these issues, we propose a fact-checking framework SLED with Self-supervised denoising evidence retrieval and LLM-Enhanced Debate-based verification. |
Jun Yang; Yuhan Bai; Dandan Song; Zhijing Wu; Yuhang Tian; |
| 265 | PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: With full input access, agents tend to focus on dominant patterns, and inter-agent communication further amplifies this bias. To tackle this issue, we propose PAMAS, a multi-agent framework with perspective aggregation, which employs hierarchical, perspective-aware aggregation to highlight anomaly cues and alleviate information drowning. |
Zongwei Wang; Min Gao; Junliang Yu; Tong Chen; Chenghua Lin; |
| 266 | Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose the Large Reasoning Embedding Model (LREM), which novelly integrates reasoning processes into representation learning. |
Jianting Tang; Dongshuai Li; Tao Wen; Fuyu Lv; Dan Ou; Linli Xu; |
| 267 | Hyperbolic Multimodal Generative Representation Learning for Generalized Zero-Shot Multimodal Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: On the other hand, there is a notable gap in the distribution of semantic similarity between seen and unseen category sets, which impacts the generative capability of the ZS-MIE models. To overcome the above disadvantages, we delve into the generalized zero-shot MIE (GZS-MIE) task and propose the hyperbolic multimodal generative representation learning framework (HMGRL). |
Baohang Zhou; Kehui Song; Rize Jin; Yu Zhao; Xuhui Sui; Xinying Qian; Xingyue Guo; Ying Zhang; |
| 268 | Rethinking Graph Generalization Through The Lens of Sharpness-Aware Minimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we focus on a prevalent yet underexplored phenomenon in graph generalization, Minimal Shift Flip (MSF)—where test samples that slightly deviate from the training distribution are abruptly misclassified. |
Yang Qiu; Yixiong Zou; Jun Wang; |
| 269 | Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We further observe that query types show consistent regularities in their alignment with retrieval paths, suggesting that routing decisions can be effectively guided by systematic rules that capture these patterns. Building on these insights, we propose a rule-driven routing framework designed specifically for hybrid-source RAG. |
Haoyue Bai; Haoyu Wang; Shengyu Chen; Zhengzhang Chen; Lu-An Tang; Wei Cheng; Yanjie Fu; Haifeng Chen; |
| 270 | GRAND: A Robust Diffusion Framework for Multi-Granularity Graph Anomaly Detection in Web Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The performance of anomaly detection in graphs of web systems is a challenge due to the sparse and camouflaged nature of such anomalies, multi-granular irregularity features, and the instability of the generative models in a real-world web application. To address these constraints, we introduce a new unified generative framework GRAND (Graph Anomaly Detection via Diffusion) suitable for graph data of the web domain. |
Maolin Wang; Beining Bao; Hongyu Chen; Zichun Liu; Lang Fu; Jun Chu; Langzhang Liang; Zenglin Xu; |
| 271 | GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) show promise in few-shot settings, their performance is often hindered by increased input size in dynamic evolving scenarios. To address these issues, we propose GORAG, a Graph-based Online Retrieval-Augmented Generation framework for dynamic few-shot text classification. |
Yubo Wang; Haoyang Li; Fei Teng; Lei Chen; |
| 272 | TopKGAT: A Top-K Objective-Driven Architecture for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, their designs are often not explicitly aligned with the top-K objective, thereby limiting their effectiveness. To address this limitation, we propose TopKGAT, a novel recommendation architecture directly derived from a differentiable approximation of top-K metrics. |
Sirui Chen; Jiawei Chen; Canghong Jin; Sheng Zhou; Jingbang Chen; Wujie Sun; Can Wang; |
| 273 | Grasp: Refining Semantic Graphs Into Purified Knowledge for Cross-Modal Communication Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Grasp, a knowledge-centric framework for cross-modal communication. |
Liang Chen; Xiaoding Wang; Limei Lin; Dajin Wang; Zhiquan Liu; Jie Wu; |
| 274 | MixRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, these systems often struggle to accurately judge the relevance of retrieved content, making them prone to distraction by irrelevant noise. To address these challenges, in this paper, we propose MixRAG, a Mixture-of-Experts Graph-RAG framework that introduces multiple specialized graph retrievers and a dynamic routing controller to better handle diverse query intents. |
Lihui Liu; Jiayuan Ding; Subhabrata Mukherjee; Carl Yang; |
| 275 | MessageShift: Fine-Grained Data Augmentation for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These approaches are context-agnostic and do not target the core computational process of GNNs: message passing. We introduce MessageShift, a novel fine-grained data augmentation paradigm that operates directly on the messages, the atomic units of information, as they flow through the GNN. |
Weigang Lu; Zheng Liang; Yaming Yang; Ziyu Zheng; Meng Yan; Beilei Ling; Ziyu Guan; Wei Zhao; |
| 276 | Beyond More Context: Retrieval Diversity Boosts Multi-Turn Intent Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We ask whether retrieval diversity, rather than longer prompts, systematically improves LLM intent understanding under fixed budgets. We present a diversity–aware retrieval framework that selects in–context exemplars to balance intent coverage and linguistic variety, and integrates this selection with standard LLM decoders; the evaluation enforces budget–matched prompts and randomized positions, and includes sensitivity analyses over exemplar count, diversity strength, and backbone size. |
Zhiming Lin; Canran Xiao; Kai Zhao; |
| 277 | How Green Is Your Login? A Cross-Protocol Benchmark of Authentication Energy \& Latency Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present AuthBench, a cross-protocol authentication benchmark measuring carbon footprint, energy, and latency across five schemes under diverse network conditions. |
Weizheng Wang; Qipeng Xie; Shiyu Wang; Qingqing Ye; Kaishun Wu; Haibo Hu; |
| 278 | Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We define this distributional divergence as linguistic fingerprint. Building on this insight, we propose LIFE (Linguistic Fingerprints Extraction), a novel detection framework that reconstructs token-level probability distributions guided by malicious prompts to capture these discriminative linguistic patterns. |
Chi Wang; Min Gao; Zongwei Wang; Junwei Yin; Kai Shu; Chenghua Lin; |
| 279 | Multi-modal Bipartite Graph Structure Learning with Information Bottleneck for Micro-video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these methods face two inherent limitations: (1) their reliance on a fixed, pre-defined graph structure makes them susceptible to noisy interactions, and (2) the multi-modal representations they learn often contain redundant information that is not discriminative enough for the recommendation task. To overcome these issues, we propose a novel Multi-modal Bipartite Graph Structure Learning network (MBGSL), which leverages the information bottleneck principle for robust micro-video recommendation. |
Ying He; Desheng Cai; Shengsheng Qian; Quan Fang; Yinwei Wei; Changsheng Xu; |
| 280 | LLMs Killed Q&A Stars? Analyzing The Impact of LLM-Generated Answers on An Online Q&A Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we conduct an in-depth analysis of how LGAs affect Naver Knowledge iN, the most popular Q&A platform in South Korea. |
Dongwon Shin; Sooel Son; |
| 281 | Re-understanding Graph Unlearning Through Memorization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing GU methods lack a clear understanding of the key factors that determine unlearning effectiveness, leading to three fundamental limitations: (1) impractical and inaccurate GU difficulty assessment due to test-access requirements and invalid assumptions, (2) ineffectiveness on hard-to-unlearn tasks, and (3) misaligned evaluation protocols that overemphasize easy tasks and fail to capture true forgetting capability. To address these issues, we establish GNN memorization as a new perspective for understanding graph unlearning and propose MGU, a Memorization-guided Graph Unlearning framework. |
Pengfei Ding; Yan Wang; Guanfeng Liu; |
| 282 | GraphTARIF: Linear Graph Transformer with Augmented Rank and Improved Focus Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We theoretically prove that these properties reduce the class separability of node representations, limiting the model’s classification ability. To address this, we propose a novel hybrid framework that enhances both the rank and focus of attention. |
Zhaolin Hu; Kun Li; Hehe Fan; Yi Yang; |
| 283 | PAGE: Progressive Anomaly Generation Network for Semi-supervised Graph Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This oversight restricts the detection of complex anomalies. In this paper, we propose a progressive anomaly generation network (PAGE) to overcome this limitation. |
Ting Guo; Dongyu Pei; Gangzhu Qiao; Kaixuan Yao; Da Wang; |
| 284 | IVQ-GNN: Mitigating Performance Gap from Graph Connection Pattern Inconsistency Via Vector Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Empirical studies indicate that this inconsistency leads to severe performance disparity. To address this issue, we propose a novel two-stage method named IVQ-GNN. |
Di Jin; Yixuan Du; Cuiying Huo; Xiaotong Huang; Ruqiong Zhang; Xiaobao Wang; Yawen Li; |
| 285 | RASTP: Representation-Aware Semantic Token Pruning for Generative Recommendation with Semantic Identifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Generative recommendation systems typically leverage Semantic Identifiers (SIDs), which represent each item as a sequence of tokens that encode semantic information. However, … |
Tianyu Zhan; Kairui Fu; Zheqi Lv; Shengyu Zhang; |
| 286 | HyperDetector: Advanced Persistent Threat Detection Via Hypergraph Neural Networks with Enhanced Global Perception Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, traditional binary edges in provenance graphs fail to represent the collaborative nature of APT attacks, where multiple entities coordinate in single operations, and local graph structures cannot capture the long-range dependencies across attack stages. To address these challenges, we propose HyperDetector, a novel hypergraph-based method for APT detection. |
Ziyue Wu; Nan Wang; Jiqiang Liu; Hairong Dong; Xibin Zhao; |
| 287 | Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose an Iterative Semantic Reasoning Framework (ISRF) for generative recommendation. |
Xiaofei Zhu; Jinfei Chen; Feiyang Yuan; Zhou Yang; |
| 288 | Tracking The Stray Sheep: Understanding DNS Response Manipulation in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We conduct large-scale measurements for 2,283 popular domains across global open DNS infrastructures. |
Wenhao Wu; Zhaohua Wang; Zihan Li; Qinxin Li; Yiming Xia; Chuan Gao; Guangxing Zhang; Zhenyu Li; |
| 289 | Allocating Chores with Restricted Additive Costs: Achieving EFX, MMS, and Efficiency Simultaneously Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We capture this with a model of restricted additive costs: every item e has a cost c(e), and each agent either incurs 0 or c(e) for e. In this work, we study how to allocate such chores fairly and efficiently. |
Zehan Lin; Xiaowei Wu; Shengwei Zhou; |
| 290 | Decentralized in Name Only: The Centralization of DAO Labor Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A fine-tuned T5 model trained for contributor recommendation largely reproduces these patterns. We therefore propose a decentralization-aware reranking method that penalizes overrepresented contributors. |
Lingling Zhang; Morteza Zihayat; Ebrahim Bagheri; |
| 291 | Rethinking The Hidden Risk of Reranking: Achieving Risk-aware Reranking with Information Gain for RAG with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: It not only narrows the potential context window for ranking the GD higher but also increases the risk of HD misleading the LLMs, potentially leading to the generation and propagation of misinformation across Web platforms. Motivated by this finding, we propose a risk-aware reranking method for RAG with LLMs, which balances the risk and benefit during reranking. |
Zhizhao Liu; Zhihua Wen; Zhiliang Tian; Zhen Huang; Miaorong Zhu; Zimian Wei; Yifu Gao; Liang Ding; Dongsheng Li; |
| 292 | KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To expose latent security vulnerabilities in GraphRAG, we propose Knowledge Evolution Poison (KEPo), a novel poisoning attack method specifically designed for GraphRAG. |
Qizhi Chen; Chao Qi; Yihong Huang; Muquan Li; Rongzheng Wang; Dongyang Zhang; Ke Qin; Shuang Liang; |
| 293 | Unveiling The Resilience of LLM-Enhanced Search Engines Against Black-Hat SEO Manipulation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present the first systematic study of SEO attacks targeting LLMSEs. |
Pei Chen; Geng Hong; Xinyi Wu; Mengying Wu; Zixuan Zhu; Mingxuan Liu; Baojun Liu; Mi Zhang; Min Yang; |
| 294 | Revisiting Graph-Level Anomaly Detection: From Partially to Fully Unsupervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose two frameworks: Score Uncertainty Learning (SUL) and Graph-data Uncertainty Learning (GUL). |
Zhenyu Yang; Ge Zhang; Shan Xue; Xiaoxiao Ma; Jian Yang; Hao Peng; Amin Beheshti; Jia Wu; |
| 295 | LEAP: LLM-Enhanced E-commerce Demand Prediction Under Emergent Events Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing approaches built on expert-designed features struggle to capture the complex, cross-city behavioral changes these events induce. To bridge this gap, we introduce LEAP, an LLM-enhanced framework that infuses comprehensive contextual reasoning into data-driven demand forecasting. |
Shuxin Zhong; Hongyu Lin; Yan Fang; Jun Chen; Kaishun Wu; |
| 296 | Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing LLM-enhanced methods often require extensive processing of detailed textual descriptions for large-scale instances or user/item entities, leading to substantial computational overhead. To address this challenge, this work introduces LLaCTR, a novel and lightweight LLM-enhanced CTR method that employs a field-level enhancement paradigm. |
Yu Cui; Feng Liu; Jiawei Chen; Xingyu Lou; Changwang Zhang; Jun Wang; Yuegang Sun; Xiaohu Yang; Can Wang; |
| 297 | HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, most current methods rely on next-item prediction with a single predicted embedding, overlooking the multifaceted nature of user interests and limiting recommendation diversity. To address these challenges, we propose HyMiRec, a hybrid multi-interest sequential recommendation framework, which leverages a lightweight recommender to extracts coarse interest embeddings from long user sequences and an LLM-based recommender to captures refined interest embeddings. |
Jingyi Zhou; Cheng Chen; Kai Zuo; Manjie Xu; Zhendong Fu; Yibo Chen; Xu Tang; Yao Hu; |
| 298 | Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose GMFlowRec, an efficient generative framework for MDSR that models domain-aware transition trajectories via Gaussian Mixture Flow Matching. |
Xiaoxin Ye; Chengkai Huang; Hongtao Huang; Lina Yao; |
| 299 | DSSmoothing: Toward Certified Dataset Ownership Verification for Pre-trained Language Models Via Dual-Space Smoothing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing dataset ownership verification (DOV) methods typically assume that watermarks remain stable during inference; however, this assumption often fails under natural noise and adversary-crafted perturbations. We propose the first certified dataset ownership verification method for PLMs under a gray-box setting (i.e., the defender can only query the suspicious model but is aware of its input representation module), based on dual-space smoothing (i.e., DSSmoothing). |
Ting Qiao; Xing Liu; Wenke Huang; Jianbin Li; Zhaoxin Fan; Yiming Li; |
| 300 | Boosting Large Language Models for Mental Manipulation Detection Via Data Augmentation and Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Detecting such behavior is challenging due to the difficult-to-annotate training data, its highly covert and multi-turn nature, and the lack of real-world datasets. To address these challenges, we propose MentalMAD, a framework that enhances large language models for mental manipulation detection. |
Yuansheng Gao; Peng Gao; Han Bao; Bin Li; Jixiang Luo; Zonghui Wang; Wenzhi Chen; |
| 301 | Lifelong Sequential Recommendation with Adaptive Subsequence Compression and Contextual Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these two types of methods still face challenges in subsequence representation learning: (1) Sequence compression methods struggle to simultaneously balance the high similarity of items within subsequences and smooth temporal continuity (i.e., small temporal intervals between adjacent items), resulting in incorrect learning of subsequence representations; (2) Top-k retrieval methods lose a large amount of effective context information when the length of the retrieved subsequence is much smaller than the original sequence, resulting in incomplete sequence representations. To overcome these challenges, we propose a novel lifelong sequential recommendation method with adaptive subsequence compression and contextual fusion. |
Fei Li; Xiaoming Liu; Jiayi Luo; Guibing Guo; Jianzhe Zhao; Xingwei Wang; |
| 302 | LLM Use for Mental Health: Crowdsourcing Users’ Sentiment-based Perspectives and Values from Social Discussions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. |
Lingyao Li; Xiaoshan Huang; Renkai Ma; Ben Zefeng Zhang; Haolun Wu; Fan Yang; Chen Chen; |
| 303 | From Social Media to Psychological Scale: An Adaptive Framework with Two-Hop Retrieval for Depression Screening Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose AdaDepression, a framework that enables explainable depression screening through a two-hop retrieval algorithm to identify symptom-relevant posts and a two-stage adaptive routing mechanism for selecting appropriate reasoning strategies and LLMs. |
Yangyang Xu; Jinpeng Hu; Peipei Song; Zhangling Duan; Xun Yang; |
| 304 | Bridging Semantic Understanding and Popularity Bias with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose FairLRM, a novel framework that bridges the gap in the semantic understanding of popularity bias with Recommendation via Large Language Model (RecLLM). |
Renqiang Luo; Dong Zhang; Yupeng Gao; Wen Shi; Mingliang Hou; Jiaying Liu; Zhe Wang; Shuo Yu; |
| 305 | Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We are the first to propose a formal economic model for a realistic user-provider ecosystem, where a user can iteratively delegate T queries to multiple model providers, and providers can engage in a range of strategic behaviors. |
Yuhan Cao; Yu Wang; Sitong Liu; Miao Li; Yixin Tao; Tianxing He; |
| 306 | Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our key idea is to reconstruct the graph information from the LLM’s graph token outputs, explicitly incorporating graph supervision to constrain the alignment process. |
Zhongjian Zhang; Xiao Wang; Mengmei Zhang; Jiarui Tan; Chuan Shi; |
| 307 | FRiskGPT: A Generative Foundation Model for Financial Risk Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: The rapid growth of online payment services has intensified financial risks, while existing models typically detect each instance of risk in isolation, which neglects inter-risk … |
Zhongjian Zhang; Mengmei Zhang; Dehua Xu; Rongjun Shi; Jianfeng Liu; Fuli Meng; Huajian Xu; Xiao Wang; Ruijia Wang; Junze Chen; Minwei Tang; Chuan Shi; |
| 308 | Counterfactual Meta-task Augmentation for Few-shot Graph Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although existing meta-learning approaches mitigate the scarcity of supervision through inter-task knowledge transfer, they remain constrained by insufficient label information and meta-task diversity under extremely weak supervision. To address this, we propose a counterfactual meta-task augmentation framework for few-shot graph learning. |
Zhiqiang Wang; Jiaxin Zhang; Chenchao Zhang; Shiying Cheng; Jianqing Liang; Peng Song; |
| 309 | GlassMiner: Mining Looking Glass Services Via Structure-Semantics Fusion for Web Observability Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present GlassMiner, a novel mining framework that fuses structural and semantic features to discover Internet-wide LG web services and the corresponding VPs. |
Yunze Wei; Xingang Shi; Han Zhang; Tianyu Zhang; Yahui Li; Xia Yin; |
| 310 | Frequency-Corrupt Based Graph Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, over-reliance on specific high-frequency signals will affect the model’s generalization. To address the above problems, we propose the Frequency-Corrupt Based Graph Self-Supervised Learning (FC-GSSL) algorithm. |
Haojie Li; Mengjiao Zhang; Guanfeng Liu; Qiang Hu; Yan Wang; Junwei Du; |
| 311 | CASE: Conflict-assessed Knowledge-sensitive Neuron Tuning for Lifelong Model Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While mainstream lifelong editing paradigms aim to alleviate knowledge forgetting through allocating and updating isolated parameter subspaces, they often overlook conflict assessment among distinct editing processes, leading to unjustified subspace allocation and indiscriminate neuron tuning. To address these issues, we propose the Conflict-Assessed Sensitive Editing (CASE) framework, which integrates a Conflict-Assessed Editing Allocation (CAA) module and a Knowledge-sensitive Neuron Tuning (KNT) strategy. |
Zhange Zhang; Yuqing Ma; Yulong Wang; Tianbo Wang; Jiakai Wang; Simin Li; Xianglong Liu; |
| 312 | From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing denoising strategies typically overlook the entity-specific nature of noise while introducing high computational costs and complex hyperparameter tuning. To address these challenges, we propose EARD (Entity-Aware Reliability-Driven Denoising), a lightweight framework that shifts the focus from interaction-level signals to entity-level reliability. |
Ze Liu; Xianquan Wang; Shuochen Liu; Jie Ma; Huibo Xu; Yupeng Han; Kai Zhang; Jun Zhou; |
| 313 | Traceable Latent Variable Discovery Based on Multi-Agent Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Huaming Du; Tao Hu; Yijie Huang; Yu Zhao; Guisong Liu; Tao Gu; Gang Kou; Carl Yang; |
| 314 | Deepfakes in The 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We analyze the 2025 Canadian federal election across X, Bluesky, and Reddit using a high-accuracy detector trained on diverse modern generative models, covering 187,778 posts. |
Victor Livernoche; Andreea Musulan; Zachary Yang; Jean-Fran\c{c}ois Godbout; Reihaneh Rabbany; |
| 315 | Guiding Generative Recommender Systems with Structured Human Priors Via Multi-head Decoding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: On the other hand, many methods targeting these beyond-accuracy objectives often require architecture-specific modifications and discard these valuable human priors by learning user intent in a fully unsupervised manner. Instead of discarding the human priors accumulated over years of practice, we introduce a backbone-agnostic framework that seamlessly integrates these human priors directly into the end-to-end training of generative recommenders. |
Yunkai Zhang; Qiang Zhang; Diji Yang; Ryan Lin; Ruizhong Qiu; Benyu Zhang; Hanchao Yu; Jason Liu; Yinglong Xia; Zhuokai Zhao; Lizhu Zhang; Xiangjun Fan; Zhuoran Yu; Abhishek Kumar; Zeyu Zheng; |
| 316 | Unveiling Backdoor Propagation in Graphs: Neuron-Centric Defense Mechanisms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we observe an interesting phenomenon: in backdoored models, specific ”backdoor neurons” (embedding dimensions) are more likely to be activated, causing nodes to be misclassified to the target label. |
Di Jin; Bingdao Feng; Xiaobao Wang; Yuxiang Zhang; Zechuan Zhang; Liang Yang; Dongxiao He; Zhen Wang; |
| 317 | When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose an automatic issue discovery method based on multimodal LLM agents. |
Chenghui Yu; Hongwei Wang; Junwen Chen; Zixuan Wang; Bingfeng Deng; Zhuolin Hao; Hongyu Xiong; Yang Song; |
| 318 | A Unified and Time-Efficient Multi-Agent Framework for Data Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Discovering related tables for join and union operations on the Web is a critical task hindered by a fundamental trade-off: existing monolithic methods are either fast but semantically shallow, or deep but computationally prohibitive. To resolve this dilemma, we introduce a unified and time-efficient multi-agent framework that reimagines table discovery as a cooperative, goal-driven process. |
Yunhao Xiao; Ying Wang; Michael Bewong; Selasi Kwashie; Xiaoxia Li; Zaiwen Feng; |
| 319 | PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose PolicySim, an LLM-based social simulation sandbox for the proactive assessment and optimization of intervention policies. |
Renhong Huang; Ning Tang; Jiarong Xu; Yuxuan Cao; Qingqian Tu; Sheng Guo; Bo Zheng; Huiyuan Liu; Yang Yang; |
| 320 | Harnessing LLM for Noise-Robust Cognitive Diagnosis in Web-Based Intelligent Education Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specially, WIES’s open environment continuously attracts new students and produces vast amounts of response logs, exacerbating the data imbalance and noise issues inherent in traditional educational systems. To address these challenges, we propose DLLM, a Diffusion-based LLM framework for noise-robust cognitive diagnosis. |
Guixian Zhang; Guan Yuan; Ziqi Xu; Yanmei Zhang; Jing Ren; Zhenyun Deng; Debo Cheng; |
| 321 | AliBoostV2: CTR-Growth Balanced Boosting Framework in Billion-Scale Recommendation Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present the CTR-growth balanced boosting framework AliBoostV2, which explicitly considers the growth value of boosting candidate users and selects optimal users to balance immediate CTR goals with long-term growth potential. |
Qijie Shen; Yuanchen Bei; Zihong Huang; Xixian Wang; Zhibo Xiao; Dimin Wang; Jialin Zhu; Yuning Jiang; Feiran Huang; Hao Chen; |
| 322 | SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To resolve the tension between digital inclusion and environmental sustainability, we introduce Sustainable Agent-Guided Expert-tuning (SAGE). |
Zhixiang Lu; Chong Zhang; Yulong Li; Angelos Stefanidis; Anh Nguyen; Imran Razzak; Jionglong Su; Zhengyong Jiang; |
| 323 | Understanding Post-Exploit Laundering Behavior on Ethereum Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper provides a behavioral perspective on post-exploit laundering on Ethereum. |
Xihan Xiong; Junliang Luo; |
| 324 | Following The TRAIL: Predicting and Explaining Tomorrow’s Hits with A Fine-Tuned LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by recent works that achieve strong recommendation performance by forecasting near-term item popularity, we propose TRAIL (TRend and explAnation Integrated Learner). |
Yinan Zhang; Zhixi Chen; Jiazheng Jing; Zhiqi Shen; |
| 325 | Accurate and Efficient Personalized Query Rewriting in Baidu Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) combined with Chain-of-Thought (CoT) capabilities provide possibilities for personalized reasoning, CoT introduces additional reasoning overhead that is difficult to accept in online scenarios requiring low latency. To address this, this paper proposes PicQue (Personalized Efficient Query Rewrite), a personalized query rewriting model training pipeline aimed at achieving high accuracy with low latency. |
Xu Chu; Angela Li; Jiaming Zhang; Wei Li; Zhijie Tan; Dawei Yin; Shuaiqiang Wang; Daiting Shi; |
| 326 | Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, we introduce an automatic pipeline to construct city-tailored location hierarchies based on Large Language Models (LLMs) and Chain-of-Thought (CoT) prompts, capturing high-level mobility semantics with minimal human verification. |
Yu Wang; Junshu Dai; Yuchen Ying; Hanyang Yuan; Zunlei Feng; Tongya Zheng; Mingli Song; |
| 327 | Fairer AI Carbon Accounting: Incorporating Market-based Attribution and Uncertainty in Embodied and Operational Carbon Footprint Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes PUMA, a Probabilistic Uncertainty Market Attribution carbon accounting model for large-scale AI models. |
Xiaoyang Zhang; Yang Deng; Fang He; Dan Wang; |
| 328 | Toward Green Computing: General Carbon Intensity Forecasting Via Dual Graph Empowered Time Series Foundation Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose DGCFM, a Dual Graph empowered Carbon-domain Foundation Model, enabling cross-regional general carbon intensity forecasting, especially for data-scarce regions. |
Xiaoyang Zhang; Taiqi Zhou; Fang He; Yang Deng; Dan Wang; |
| 329 | QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present QuiZSF, a retrieval-augmented forecasting framework that integrates search and forecasting for time series data. |
Shichao Ma; Zhengyang Zhou; Qihe Huang; Binwu Wang; Yang Wang; |
| 330 | Understanding Strategic Platform Entry and Seller Exploration: A Stackelberg Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We formulate a Stackelberg model, where the platform acts as the leader by committing to an entry policy: when will it enter and compete on a product? |
Garrett Seo; Xintong Wang; David C. Parkes; |
| 331 | P-Aligner: Pre-Aligning LLMs Via Principled Instruction Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large Language Models (LLMs) can fail to align with human preference on flawed instructions, leaving a large room for pre-aligning instructions before the inference. In this work, we show that such pre-alignment can be efficiently and effectively achieved by P-Aligner, a lightweight module generating instructions that hold the original intents but are expressed in a human-preferred way. |
Feifan Song; Bofei Gao; Yifan Song; Yi Liu; Weimin Xiong; Yuyang Song; Tianyu Liu; Guoyin Wang; Houfeng Wang; |
| 332 | Bowling with ChatGPT: On The Evolving User Interactions with Conversational AI Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these macro-level observations offer limited insight into how the purpose of these interactions shifts over time, how users frame their interactions with the system, and how steering dynamics unfold in these human-AI interactions. To examine these evolving dynamics, we gathered and analyzed a unique dataset InVivoGPT: consisting of 825K ChatGPT interactions, donated by 300 users through their GDPR data rights. |
Sai Keerthana Karnam; Abhisek Dash; Krishna Gummadi; Animesh Mukherjee; Ingmar Weber; Savvas Zannettou; |
| 333 | Greedy Attack: Breaking Finality Against VeChain Proof-of-Authority Consensus Protocol Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study the finality approach by VeChainThor, the consensus protocol of VeChain blockchain. |
Rujia Li; Qin Wang; Haochen Wang; Xueqian Lu; Sisi Duan; |
| 334 | Unsupervised Subgraph Anomaly Detection Based on Pattern Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current studies rely on traditional node detection methods and fixed sampling strategies of subgraph structures, which makes it difficult to learn the pattern collaboration behavior of subgraphs. To address this limitation, this paper proposes a novel unsupervised framework named PC-SAD. |
Jiayang Sun; Shenghao Liu; Xianjun Deng; Wei Xiang; Meng Luo; Qiankun Zhang; Dandan Zheng; |
| 335 | XInsight: Integrative Stage-Consistent Psychological Counseling Support Agents for Digital Well-Being Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Web-based platforms are becoming a primary channel for psychological support, yet most LLM-driven chatbots remain opaque, single-stage, and weakly grounded in established therapeutic practice. To address this gap, we present XInsight, a multi-agent framework that models psychological support as a stage-consistent workflow aligned with the classical Exploration-Insight-Action paradigm. |
Fei Wang; Jiangnan Yang; Junjie Chen; Yuxin Liu; Kun Li; Yanyan Wei; Dan Guo; Meng Wang; |
| 336 | Efficient High-Dimensional Time Series Forecasting with Transformers: A Channel Reordering Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, attention weights reveal that inter-channel dependencies exhibit both local clusters and global structures, yet current methods fail to disentangle these heterogeneous patterns, resulting in mutual interference and degraded forecasting accuracy. To address these challenges, we propose a novel Channel Reordering-Aligned group Fusion Transformer (CRAFT) for high-dimensional time series forecasting. |
Yuchen Fang; Shiyu Wang; Yuxuan Liang; Zhou Ye; Yang Xiang; Yan Zhao; Kai Zheng; |
| 337 | Unmasking Bots in Higher Dimensions: Message Passing Over Simplexes for Bot Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns, and their multi-relation fusion strategies are typically simplistic, ignoring dependencies between relations and user-specific preferences. To overcome these limitations, we propose MPS-Bot, a model that integrates higher-order structure modeling with user-specific cross-relation dependency learning. |
Fangfang Li; Huihui Zhang; Xin Zhang; Wei Wu; |
| 338 | From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Both limitations can misguide reasoning and compromise answer reliability. To overcome these challenges, we propose MedRGAG, a unified retrieval–generation augmented framework that seamlessly integrates external and parametric knowledge for medical QA. |
Lei Li; Xiao Zhou; Yingying Zhang; Xian Wu; |
| 339 | Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces an orchestration-free framework using Task-Oriented Flowcharts (TOFs) to enable end-to-end automation without manual intervention. |
Mengze Hong; Chen Jason Zhang; Zichang Guo; Hanlin Gu; Di Jiang; Qing Li; |
| 340 | Spattack: Subgroup Poisoning Attacks on Federated Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To achieve a better trade-off, we propose enhanced approximation and promotion strategies. |
Bo Yan; Yurong Hao; Dingqi Liu; Huabin Sun; Pengpeng Qiao; Wei Yang Bryan Lim; Yang Cao; Chuan Shi; |
| 341 | Reinforcement Learning with Verbalized Probabilities for LLM Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods to elicit probabilities from LLMs either sacrifice their crucial Chain-of-Thought (CoT) reasoning capabilities or suffer from poor calibration. To address this, we introduce a new paradigm, Verbalized Probability Distribution, and a novel training framework, RLVP (Reinforcement Learning with Verbalized Probabilities). |
Liyao Li; Hao Chen; Jiaming Tian; Wentao Ye; Lirong Gao; Chao Ye; Ningtao Wang; Xing Fu; Yu Cheng; Haobo Wang; Gang Chen; Junbo Zhao; |
| 342 | EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This complexity substantially increases optimization difficulty, rendering models prone to stagnation at saddle points within high-dimensional parameter spaces. To address these issues, we propose a lightweight Transformer architecture in conjunction with a novel Escape-Explore Optimizer (EEO). |
Hua Wang; Jinghao Lu; Fan Zhang; |
| 343 | Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This refers to the fine-grained dependencies embedded within the sequence that span across different time steps, which is especially prevalent in regular Web data. To fundamentally address this issue, we propose a new perspective on time series embedding. |
Fan Zhang; Shiming Fan; Hua Wang; |
| 344 | LLMQuA: Practical Backdoor Injection on Large Language Model Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We evaluate LLMQuA on representative LLMs and two important attack scenarios: evasion of content moderation and causing systematic refusal of benign user queries. |
Xiangxiang Chen; Peixin Zhang; Jun Sun; Jin Song Dong; Wenhai Wang; Jingyi Wang; |
| 345 | LoRA-E2: Effective and Efficient Low-rank Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose LoRA-E2, which utilizes a Gaussian initialization with variance Θ(n-3/4 ) for A, and employs the Gauss-Seidel iteration to train B and A. |
Shengkun Zhu; Jinshan Zeng; Yiming Wang; Sheng Wang; Yuan Sun; Shangfeng Chen; Yuan Yao; Qiang Yang; |
| 346 | MCoT-MVS: Multi-level Vision Selection By Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning (MCoT-MVS) for CIR, integrating attention-aware multi-level vision features guided by reasoning cues from a multi-modal large language model (MLLM). |
Xuri Ge; Chunhao Wang; Xindi Wang; Zheyun Qin; Zhumin Chen; Xin Xin; |
| 347 | Dual-Branch Multi-Granularity Network with Structured Contrastive Ranking for Cross-Modal Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Cross-modal retrieval (CMR) has advanced considerably by mapping image and text features into a shared embedding space; however, these approaches still face two persistent challenges: (1) semantic sparsity, where discriminative cues are confined to localized regions, making it difficult to identify implicit visual evidence; and (2) ranking uncertainty under semantic ambiguity, where models struggle to maintain the correct retrieval order when candidates share similar contexts. To address these issues, we propose the Dual-Branch Multi-Granularity Network (DBMG) with Structured Contrastive Ranking, which enriches visual semantics by leveraging a multimodal large language model to generate auxiliary descriptions, aligns sparse cues through a dual-branch architecture capturing both global and local interactions, and enforces ranking consistency via a three-stage contrastive objective that progressively optimizes category clustering, instance alignment, and margin-based ranking. |
Zihao Chen; Chenyang Bu; Shengwei Ji; Xindong Wu; |
| 348 | Modeling Point-to-Point Dependency for High-Dimensional Long-Term Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we propose replacing Self-Attention with autocorrelation, achieving two key innovations: 1) We propose calculating autocorrelation across both variable and time dimensions, which is a global paradigm, to model point-to-point dependencies. |
Xinyu Li; Kexi Chen; Ying Zheng; Zhiyi Yao; Yi Xie; Jihan Dai; Lei Bai; Jin Zhao; Jiajie Shen; Yunqi Cai; Hong Lu; Xin Wang; |
| 349 | Personalized Parameter-Efficient Fine-Tuning of Foundation Models for Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, even with PEFT, item embeddings from multimodal foundation models remain user-blind: item embeddings are not conditioned on user interests, despite the fact that users with diverse interests attend to different item aspects. To address this limitation, we propose PerPEFT, a personalized PEFT strategy for multimodal recommendation. |
Sunwoo Kim; Hyunjin Hwang; Kijung Shin; |
| 350 | FeedGuard: Online Critic-Guided Reinforcement Learning with Privacy-Preserving Feedback for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose FeedGuard, a critic-guided reinforcement learning framework with privacy-preserving feedback. |
Mengying Zhu; Feiyue Chen; Lifan Jiang; Mengyuan Yang; Yangyang Wu; Guanjie Cheng; Xiaolin Zheng; |
| 351 | Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Zhaoqi Wang; Daqing He; Zijian Zhang; Ye Liu; Jiamou Liu; Zhirui Zeng; Zhan Qin; Zhen Li; Xin Li; Hongwei Yao; Jincheng An; Yong Liu; Yi Li; Qi Sun; Xiulei Liu; Liehuang Zhu; |
| 352 | FediScan: Collaborative Social Bot Detection in The Fediverse Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current social bot detection methods, designed for centralized systems, fail to address these challenges while preserving user privacy. To fill this gap, we propose FediScan, a decentralized federated learning framework for social bot detection in the Fediverse. |
Min Gao; Wen Wen; Haoran Du; Qiang Duan; Yu Xiao; Yupeng Li; Xin Wang; Pan Hui; Yang Chen; |
| 353 | ATGFB-MFF: Adaptive Text-Guided Fiber Bundle Feature Fusion with LLMs for Multimodal Sentiment Analysis and Emotion Recognition in Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, when applied to multimodal sentiment data, LLMs face significant challenges, including the inability to directly process heterogeneous data, difficulties in coping with feature misalignment and suboptimal cross-modal fusion. To address these challenges, we propose a novel multimodal sentiment inference framework named ATGFB-MFF which grounded in fiber bundle theory. |
Zhaowei Liu; Sheng Liu; Weiqing Yan; Peng Song; Yongchao Song; Rufei Gao; |
| 354 | Multi-field Balance-aware Calibration of Predictions in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, current field-aware approaches are typically limited to single-field calibration and fail to account for field sensitivity. To overcome these limitations, we propose a Pareto Frontier-based Multi-field Personalized Calibration (PF-MPC) method which formulates multi-field calibration as a multi-objective optimization problem. |
Zi-Kang Wang; Lei Gong; Shu-Ting Shi; Lan-Zhe Guo; Mu-Yu Zhang; |
| 355 | Cascaded Verification Framework: A Progressive Approach for Mitigating Hallucinations in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through extensive analysis on factoid question answering tasks, we observe that the complexity of queries varies significantly, with simpler questions often achievable by single models with high confidence, while only complex or ambiguous queries benefit from multi-model verification. Leveraging this insight, we propose the Cascaded Verification Framework (CVF), a progressive approach that dynamically determines the necessary level of verification based on uncertainty signals. |
Shu Zhou; Yufei Song; Jinman Leng; Xin Wang; Tao Fan; Hao Wang; |
| 356 | Activation Caching for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In-context methods suffer from increased computational costs and degraded performance with longer contexts, while parametric approaches require extensive offline training and lack the flexibility to adapt knowledge representations based on queries. To address these challenges, we introduce ACT-RAG (Activation Caching for Retrieval-Augmented Generation), a novel RAG paradigm that pre-computes and caches document activation patterns across model layers. |
Shu Zhou; Yufei Song; Jinman Leng; Xin Wang; Tao Fan; Hao Wang; |
| 357 | LaV-CoT: Language-Aware Visual CoT with Multi-Aspect Reward Optimization for Multilingual Text-Centric VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing approaches rely primarily on textual Chain-of-Thought (CoT) and provide limited support for multilingual multimodal reasoning. To address this gap, we introduce LaV-CoT, the first Language-aware Visual CoT framework with Multi-Aspect Reward Optimization. |
Jing Huang; Zhiya Tan; Shutao Gong; Fanwei Zeng; Joey Tianyi Zhou; Changtao Miao; Huazhe Tan; Weibin Yao; Jianshu Li; |
| 358 | CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CompactRAG, a simple yet effective framework that decouples offline corpus restructuring from online reasoning. |
Hao Yang; Zhiyu Yang; Xupeng Zhang; Wei Wei; Yunjie Zhang; Lin Yang; |
| 359 | SIT-KGED: Simply Inject Topology Into LLM for Knowledge Graph Error Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we extend KGED to a four-class classification task to identify which element is incorrect or whether the triple is correct. |
Ting Li; Xingyi Mao; Yipeng Yu; Liang Yao; |
| 360 | TargetMR: Learning Modality Target for Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing MRSs overlooked this distinction, failing to prevent auxiliary objects from dominating the representation, leading to biased item representation. To address this issue, we propose a model-agnostic framework ”TargetMR”. |
Gu Tang; Jinghe Wang; Jiang Bo; Ze Zhao; Jianping Zhou; Xiaoying Gan; Luoyi Fu; Xinbing Wang; Chenghu Zhou; |
| 361 | Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Two web-native threats are especially concerning: indirect prompt injection and retrieval poisoning, where malicious instructions or biased content survive ingestion and influence retrieval or generation. We introduce OpenRAG-Soc, a compact, reproducible benchmark and harness for evaluating web-facing RAG pipelines under these threats as a discrete data package. |
Haoze Guo; Ziqi Wei; |
| 362 | Multi-Modal Enhanced Graph Transfer Learning for Digital Finance Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing graph transfer learning methods struggle to model such multi-modal dependencies and to align divergent feature distributions. To tackle these challenges, we develop a Multi-mOdal Enhanced Graph Transfer Learning (MOE-GTL) framework which incorporates graph, temporal, and textual modalities for fraudulent node detection. |
Yuxin Liu; Stephen Chan; Jeffrey Chu; Yuanyuan Zhang; Chenguang Yang; Zihao Wang; Yulia R. Gel; Yuzhou Chen; |
| 363 | Interpretable Dynamic Network Modeling of Tensor Time Series Via Kronecker Time-Varying Graphical Lasso Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose Kronecker Time-Varying Graphical Lasso (KTVGL), a method designed for modeling tensor time series. |
Shingo Higashiguchi; Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai; |
| 364 | Trust on Reload: Securing Browser-Based End-to-End Encryption Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For each identified pitfall, we present concrete attack scenarios. |
Simon Schwarz; Florian Bauckholt; Leon Trampert; |
| 365 | From Criteria to Ranking: Targeting-Aware Tripartite Graph Learning for Multi-Criteria Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing multi-criteria recommendation methods often fail to explicitly model user-specific preferences across criteria or to incorporate item–criterion signals into representation learning. To solve these problems, we propose TaTriGR, a Targeting-Aware Tripartite Graph Recommender, which jointly models user–item–criterion interactions in a unified tripartite graph and integrates user-specific criterion weights through targeting mechanisms. |
Zhenhua Meng; Fanshen Meng; |
| 366 | IQ-Guard: An Effective and Noise-Resistant Framework for Graph Fraud Detection on IQIYI Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we develop iQ-Guard, an effective and noise-resistant framework for graph fraud detection that has been fully deployed on iQIYI, one of China’s largest online video and social platforms. |
Yuting Huang; Ziquan Fang; Zhengjie Zhou; Tinghui Luo; Lu Chen; Surun Ji; Huimei Zheng; Mingfan Lu; Fangshu Chen; Yunjun Gao; |
| 367 | PI2I: A Personalized Item-Based Collaborative Filtering Retrieval Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional methods, such as item-to-item collaborative filtering (CF) and two-tower models, often fall short in capturing the complex user-item interactions due to uniform truncation strategies and overdue user-item crossing. To address these limitations, we propose Personalized Item-to-Item (PI2I), a novel two-stage retrieval framework that enhances the personalization capabilities of CF. In the first Indexer Building Stage (IBS), we optimize the retrieval pool by relaxing truncation thresholds to maximize Hit Rate, thereby temporarily retaining more items users might be interested in. |
Shaoqing Wang; Yingcai Ma; Kairui Fu; Ziyang Wang; Dunxian Huang; Yuliang Yan; Jian Wu; |
| 368 | Anomaly Detection of Interaction Behaviors in Streaming Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a novel evaluation metric, namely Interaction Willingness, to measure the propensity for entity interactions. |
Shuai Ren; Fan Zhang; Bolin Wang; Xiang Zhao; Zhihong Tian; |
| 369 | Acting Flatterers Via LLMs Sycophancy: Combating Clickbait with LLMs Opposing-Stance Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Chaowei Zhang; Xiansheng Luo; Zewei Zhang; Yi Zhu; Jipeng Qiang; Longwei Wang; |
| 370 | LSIG: Long Semantic IDs for Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current methods are typically limited to short semantic IDs (e.g., just 3-4 tokens), which imposes a bottleneck on hierarchical expressiveness and hinders the accuracy and diversity of sequential recommendations. To overcome this limitation, we propose LSIG, a hierarchy-aware model tailored for long semantic ID modeling. |
Zhao Li; FengYang Qi; Chuanyu Xu; Tao Zhang; Chengfu Huo; Peng Zhang; |
| 371 | Emergence of Structural Disparities in The Web of Scientific Citations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We find that women receive systematically fewer citations than men, and that attention is increasingly concentrated among authors from elite institutions—patterns not fully explained by underrepresentation alone. To explain these dynamics, we introduce a model of citation network growth that incorporates homophily (tendency to cite similar authors), preferential attachment (favoring highly cited authors) and group size (underrepresentation). |
Buddhika Nettasinghe; Nazanin Alipourfard; Vikram Krishnamurthy; Kristina Lerman; |
| 372 | RMBRec: Robust Multi-Behavior Recommendation Towards Target Behaviors Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose Robust Multi-Behavior Recommendation towards Target Behaviors (RMBRec), a robust multi-behavior recommendation framework grounded in an information-theoretic robustness principle. |
Miaomiao Cai; Zhijie Zhang; Junfeng Fang; Zhiyong Cheng; Xiang Wang; Meng Wang; |
| 373 | WeaveRec: An LLM-Based Cross-Domain Sequential Recommendation Framework with Model Merging Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet, our empirical study shows that naively training an LLM on combined domains—or simply merging several domain-specific LLMs—often degrades performance relative to a model trained solely on the target domain.To address these challenges, we first experimentally investigate the cause of suboptimal performance in LLM-based cross-domain recommendation and model merging. Building on these insights, we introduce WeaveRec, which cross-trains multiple LoRA modules with source and target domain data in a ”weaving” fashion, and fuses them via model merging. |
Min Hou; Xin Liu; Le Wu; Chenyi He; Hao Liu; Zhi Li; Xin Li; Si Wei; |
| 374 | Personalized Federated Fine-Tuning for LLMs Via Data-Driven Heterogeneous Model Architectures Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Existing approaches to federated LLM fine-tuning usually adopt a uniform model architecture, making it challenging to fit highly heterogeneous client-side data in varying domains and tasks, e.g., hospitals and financial institutions conducting federated fine-tuning may require different LLM architectures due to the distinct nature of their domains and tasks. To address this, we propose FedAMoLE, a lightweight personalized FL framework that enables data-driven heterogeneous model architectures. |
Yicheng Zhang; Zhen Qin; Zhaomin Wu; Jian Hou; Shuiguang Deng; |
| 375 | ProvGuard: Logic-Aware Multi-View Contrastive Learning for Robust and Efficient Host Threat Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, recent studies show that these graph-driven methods remain vulnerable to graph manipulation attacks, where adversaries subtly alter provenance graphs to evade detection, which limits their practical deployment. To address this challenge, we present ProvGuard, a robust anomaly detection framework that couples logic-aware multi-view augmentation with contrastive representation learning. |
Anyuan Sang; Li Yang; Lu Zhou; Cheng Zhou; Junbo Jia; Huipeng Yang; |
| 376 | WebGeoInfer: Structure-Free Multi-Stage Framework for Geolocation Inference from Exposed Device Web Interfaces Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose WebGeoInfer, a framework that does not rely on page structure. |
Huipeng Yang; Li Yang; Lu Zhou; Lichuan Ma; Xinyue Wang; Junbo Jia; Anyuan Sang; |
| 377 | DMAP: Human-Aligned Structural Document Map for Multimodal Document Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such flattening disrupts logical and spatial dependencies—such as section organization, figure-text correspondence, and cross-reference relations—that humans naturally exploit for comprehension. To address this limitation, we introduce a document-level structural Document MAP (DMAP), which explicitly encodes both hierarchical organization and inter-element relationships within multimodal documents. |
Shunliang Fu; Yanxin Zhang; Yixin Xiang; Xiaoyu Du; Jinhui Tang; |
| 378 | Rethinking Implicit Hate Speech Detection: Focusing on Latent Hate Components Via Dual-Process Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose DuPL, a Dual-Process argumentation framework that centers detection on LHCs. |
Shiqi Sun; Du Su; Wei Chen; Xueqi Cheng; |
| 379 | Has The Two-Decade-Old Prophecy Come True? Artificial Bad Intelligence Triggered By Merely A Single-Bit Flip in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By building an information-theoretic weight sensitivity entropy model and a probabilistic heuristic scanning framework called BitSifter, we achieved efficient localization of critical vulnerable bits in models with hundreds of millions of parameters. |
Yu Yan; Siqi Lu; Yang Gao; Zhaoxuan Li; Ziming Zhao; Qingjun Yuan; Yongjuan Wang; |
| 380 | Long-Tailed Recognition of Evidential Experts for Graph-level Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, the predictions of existing algorithms are usually not trustworthy, and the trained classifiers remain ignorant to their predictive confidence. Towards this end, in this paper we develop a principled framework called GraphEVER for long-tailed graph-level classification. |
Wei Ju; Siyu Yi; Zhengyang Mao; Yifang Qin; Yifan Wang; Zhiping Xiao; Yiwei Fu; Ziyue Qiao; Ming Zhang; |
| 381 | Automated Model Selection for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a novel, efficient, and scalable MTSF model selection method that directly selects suitable MTSF methods based on data characteristics without extensive model training. |
Xiaoxuan Fan; Jiaqi Sun; Xianjun Deng; Qiankun Zhang; Wei Xiang; Shenghao Liu; Lingzhi Yi; |
| 382 | Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This design can also compromise entity representation information from all KG domains, impeding performance improvements, especially in low-resource data scenarios. To address this, we pioneer a generation-based paradigm for MKGC and propose DMKGC, a conditional diffusion-guided knowledge transfer framework. |
Jiawei Sheng; Taoyu Su; Xixun Lin; Xiaodong Li; Tingwen Liu; |
| 383 | Beyond Patches: Superpixel Token-based Transformers for Attribute-Specific Fashion Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing patch-based attention and Transformer methods often misalign with irregular attribute regions and are prone to background noise, limiting their ability to capture subtle, pixel-level microstructures. To tackle these challenges, we propose Super Fashion. |
Shuili Zhang; Hongzhang Mu; Wenyuan Zhang; Duohe Ma; Tingwen Liu; |
| 384 | Enhancing Content Moderation with LLMs: A Reddit Case Study on Evaluating and Refining Human Decisions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large Language Models (LLMs) offer significant potential for assisting with the design and implementation of social platform moderation. |
Jiahui He; Yiluo Wei; Gareth Tyson; |
| 385 | TrueLens: Video Fake News Detection with Dual Level Evidence Gathering and Consolidation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose TrueLens, a new framework for video fake news detection that gathers and consolidates dual-level evidence zznotethrough three primary components, ie, External Precedent Retriever, Adversarial Contrastor, and Internal Evidential Logic Fusion. |
Junyi Chen; Qian Liu; Jing Sun; Yi Zhang; |
| 386 | BitHeteroNet: A Heterogeneous Network Benchmark for Enhanced Anomaly Detection in Bitcoin Transactions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the absence of a comprehensive benchmark that accurately reflects UTXO-based transactional behavior, we introduce BitHeteroNet, a novel benchmark designed to support robust Bitcoin fraud detection. |
Zheng Gong; Shuheng Shen; Changhua Meng; Ying Sun; |
| 387 | The Double-Edged Sword of Knowledge Transfer: Diagnosing and Curing Fairness Pathologies in Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nonetheless, emerging evidence shows that CDR can inadvertently heighten group-level unfairness. In this work, we conduct a comprehensive theoretical and empirical analysis to uncover why these fairness issues arise. |
Yuhan Zhao; Weixin Chen; Li Chen; Weike Pan; |
| 388 | Linguistic Signatures for Enhanced Emotion Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study examines whether linguistic features can serve as reliable interpretable signals for emotion recognition in text. |
Florian Lecourt; Madalina Croitoru; Konstantin Todorov; |
| 389 | MASI: Memory-Adaptive Inference Framework for Spiking Neural Networks on Edge Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While prior work explores lightweight model design and system-level memory management, these methods either sacrifice accuracy or incur high runtime overhead due to timestep-dependent dynamics. To tackle these challenges, we propose a memory-adaptive framework MASI that enables efficient on-device SNN inference by combining (1) a fine-grained memory-adaptive layer slicing strategy, (2) a timestep-agnostic scheduler that maximizes memory utilization with minimal fragmentation, and (3) a timestep-aware early-exit mechanism that reduces redundant calculations. |
Di Yu; Helin Zheng; Changze Lv; Xin Du; Linshan Jiang; Xiang Liu; Gang Pan; Shuiguang Deng; |
| 390 | Structure-Semantic Synergized Deep Contrastive Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current contrastive graph clustering approaches suffer from insufficient integration of structural and semantic information, coupled with the absence of reliable sample selection strategies. To address these dual limitations, we introduce Structure-Semantic Synergized Deep Contrastive Graph Clustering (S³-DCGC), a novel framework that jointly models topological structure and semantic features through two synergistic mechanisms. |
Shifei Ding; Zhe Li; Xiao Xu; Chao Li; |
| 391 | Med-R2: Crafting Trustworthy LLM Physicians Via Retrieval and Reasoning of Evidence-Based Medicine Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These issues collectively prevent LLMs from demonstrating the expected level of proficiency in mastering medical expertise. To address these challenges, we introduce **Med-R2**, a novel LLM physician framework that adheres to the *Evidence-Based Medicine (EBM)* process, efficiently integrating retrieval mechanisms as well as the selection and reasoning processes of evidence, thereby enhancing the problem-solving capabilities of LLMs in healthcare scenarios and fostering a trustworthy LLM physician. |
Lu Keer; Zheng Liang; Da Pan; Shusen Zhang; Guosheng Dong; Huang Leng; Bin Cui; Zhonghai Wu; Wentao Zhang; |
| 392 | Graph Poisoning for Node Rank Manipulation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a black-box graph poisoning attack that degrades a target node’s ranking using only edge deletions, without access to model parameters, gradients, or retraining. |
Seyed Mohammad Hosseini; Radin Hamidi Rad; Morteza Zihayat; Ebrahim Bagheri; |
| 393 | TGweaver: Synthesizing Transaction Graphs for De-anonymization Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Fajie Wu; Jiajing Wu; Zhiying Wu; Jun Chen; Tao Wang; Longjian He; Bowen Song; Weiqiang Wang; |
| 394 | Dual-View Hypercube Alignment Framework for Open-World Entity Typing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To accommodate the open-world requirement, we investigate a new and critical task: open-world entity typing (OWET), which aims to simultaneously classify entity mentions within the known type ontology and discover mention clusters of unknown types. To tackle this task, we propose a Dual-View Hypercube Alignment (DVHA) framework, which leverages diverse tailor-designed alignment mechanisms based on hypercubes to jointly capture and combine complementary knowledge involved in the ontology and instance views for high-quality mention representation learning. |
Hu Chen; Yu Zhu; Wei Shen; |
| 395 | DAPWeb: Construct-Aligned Evaluation of MLLMs for Web-Based Child Mental Screening Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, their ability to deliver construct-level clarity and interpretive consistency for responsible deployment has not been systematically evaluated. To address this gap, we propose DAPWeb, a construct-aligned evaluation framework that assesses whether MLLMs can reliably support DAP-based child mental screening in Web environments. |
Rui Guo; Fengyi Wang; Ling-Yu Lin; Guolong Wang; |
| 396 | Bridging Visual Dynamics and Narrative Reasoning: Multimodal Large Language Models for Short Drama Quality Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Likewise, other video understanding techniques tend to be event-centric, failing to adequately connect narrative elements with visual content. To bridge this gap between visual dynamics and narrative reasoning, we propose a user-centric quality indicator alongside an automated pipeline for constructing a Chain-of-Thought (CoT) dataset. |
Qingyang Liu; Jiangtong Li; Zelin Peng; Shaobo Wang; Zhaohe Liao; Shuochen Chang; Bingjie Gao; Haonan Zhao; Mu Liu; Jidong Jiang; Li Niu; |
| 397 | RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, (i) random edge perturbations often distort critical structural signals and degrade semantic consistency across augmented views, and (ii) data sparsity hampers the propagation of collaborative signals, limiting generalization. To tackle these challenges, we propose RaDAR (Relation-allowbreaka ware Diffusion-allowbreakAsymmetric Graph Contrastive Learning Framework for Recommendation Systems), a novel framework that combines two complementary view generation mechanisms: a graph generative model to capture global structure and a relation-aware denoising model to refine noisy edges. |
Yixuan Huang; Jiawei Chen; Shengfan Zhang; Zongsheng Cao; |
| 398 | AgentSense: LLMs Empower Generalizable and Explainable Web-Based Participatory Urban Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce AgentSense, a hybrid, training-free framework that integrates large language models (LLMs) into participatory urban sensing through a multi-agent refinement system. |
Xusen Guo; Mingxing Peng; Xixuan Hao; Xingchen Zou; Qiongyan Wang; Sijie Ruan; Yuxuan Liang; |
| 399 | We May Not Need Much Visual Encoding of Web Data for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these improvements require excessive visual token encoding, resulting in high memory usage and computational costs. To address this limitation, we propose LightVAM, a plug-and-play lightweight visual augmented model that adaptively adjusts visual encoding according to input complexity. |
Tan Yue; Qiong Wu; Dongyan Zhao; |
| 400 | Dir-GD: Directed Graph Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although graph sampling and distillation techniques alleviate these issues by subsampling or creating surrogate graphs, they primarily handle undirected graphs, neglecting directional semantics that are crucial for applications like fraud detection and causal analysis. To address these limitations, we introduce the Directed Graph Distillation (Dir-GD) framework, which combines distributed learning with community detection to divide large directed graphs into independent subgraphs for distributed directed GNN training. |
Ce Yang; Fei Hao; Jie Gao; Jianrui Chen; Jia Hu; Geyong Min; |
| 401 | Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While many deep learning models have achieved promising performance, few consider how to identify and penalize low-predictability samples to improve model performance from the training perspective. To fill this gap, we propose a general Amortized Predictability-aware Training Framework (APTF) for both TSF and TSC. |
Xu Zhang; Peng Wang; Yichen Li; Wei Wang; |
| 402 | SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies challenging. To address this, we propose SEMixer, a lightweight multiscale model designed for long-term TSF. |
Xu Zhang; Qitong Wang; Peng Wang; Wei Wang; |
| 403 | PFedDKS: Detached Knowledge Sharing for Personalized Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Based on the theory of neural collapse, in this paper, we demonstrate both theoretically and empirically that the feature extractor should be partially shared rather than fully shared. |
Haozhao Wang; Wenchao Xu; Jingzhi Wang; Yunfeng Fan; Xiaoquan Yi; Rui Zhang; |
| 404 | Structure Over Scale: Diagnosing Liquidity Fragility in Concentrated-Liquidity AMMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate whether liquidity stability in Concentrated Liquidity Automated Market Makers (CLAMMs) is determined by how liquidity is structured (who owns it and where it is placed) rather than by its total value locked (TVL). |
Qiangqiang Liu; Runfa Jiang; Qian Huang; Frank Fan; Kunpeng Ren; Wei Cai; |
| 405 | KEGOD: Kernel-enhanced Latent Substructure Learning for Graph Out-Of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: On the other hand, acquiring labeled data for graph learning is typically time-consuming and labor-intensive. Toward this end, in this paper, we propose a novel kernel-enhanced graph substructure learning framework termed KEGOD for unsupervised graph OOD detection. |
Yifan Wang; Haodong Zhang; Zhiping Xiao; Yusheng Zhao; Siyu Yi; Nan Yin; Xinwang Liu; Ming Zhang; Wei Ju; |
| 406 | Make It Long, Keep It Fast: End-to-End 10k-Sequence Modeling at Billion Scale on Douyin Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present an end-to-end system that scales long-sequence modeling to 10k -length histories in production. |
Lin Guan; Jia-Qi Yang; Zhishan Zhao; Beichuan Zhang; Bo Sun; Xuanyuan Luo; Jinan Ni; Xiaowen Li; Yuhang Qi; Zhifang Fan; Hangyu Wang; Qiwei Chen; Yi Cheng; Feng Zhang; Xiao Yang; |
| 407 | An LLM-Powered Cooperative Framework for Large-Scale Multi-Vehicle Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing path search algorithms and reinforcement learning methods struggle to scale to city-wide networks, often failing to capture the nonlinear, stochastic, and coupled dynamics of urban traffic. To address these challenges, we propose CityNav, a hierarchical, LLM-powered framework for large-scale multi-vehicle navigation. |
Yuping Zhou; Siqi Lai; Jindong Han; Hao Liu; |
| 408 | RAIE:Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing model update strategies mainly rely on global fine-tuning or pointwise editing, but they face two fundamental challenges: (i) imbalanced update granularity, where global updates perturb behaviors unrelated to the target while pointwise edits fail to capture broader preference shifts; (ii) unstable incremental updates, where repeated edits interfere with prior adaptations, leading to catastrophic forgetting and inconsistent recommendations. To address these issues, we propose Region-Aware Incremental Editing (RAIE), a plug-in framework that freezes the backbone model and performs region-level updates. |
Jin Zeng; Yupeng Qi; Hui Li; Chengming Li; Ziyu Lyu; Lixin Cui; Lu Bai; |
| 409 | SIsomap: Secure Collaborative Manifold Learning with Reducing Communication Costs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on a classic manifold learning technique, known as isometric mapping (Isomap), and propose SIsomap, the first secure, distributed manifold learning system. |
Peizhao Zhou; Xiaojie Guo; Pinzhi Chen; Ranyang Liu; Lihai Nie; Tong Li; Zheli Liu; |
| 410 | Beyond Words: Enhancing Desire, Emotion, and Sentiment Recognition with Non-Verbal Cues Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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). |
Wei Chen; Tongguan Wang; Feiyue Xue; Junkai Li; Hui Liu; Ying Sha; |
| 411 | AgriGPT-Omni: A Unified Speech–Vision–Text Framework for Multilingual Agricultural Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite rapid advances in multimodal large language models, agricultural applications remain constrained by the lack of multilingual speech data, unified multimodal architectures, and comprehensive evaluation benchmarks. To address these challenges, we present AgriGPT-Omni, an agricultural omni-framework that integrates speech, vision, and text in a unified framework. |
Bo Yang; Lanfei Feng; Yunkui Chen; Yu Zhang; Jianyu Zhang; Xiao Xu; Nueraili Aierken; Shijian Li; |
| 412 | DTRec: Learning Dynamic Reasoning Trajectories for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These rigidity lead to suboptimal performance and significant computational waste. To overcome these challenges, we propose DTRec, a novel and effective framework that explores the Dynamic reasoning Trajectory for Sequential Recommendation along both direction and depth. |
Yifan Shao; Peilin Zhou; Shoujin Wang; Weizhi Zhang; Sunghun Kim; Xu Cai; |
| 413 | Wiseswap: Elastic Datacenter Network-Aware Disaggregated Memory for Multi-Tenant Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing DMS designs rely on idealized assumptions-overlooking interference from co-located RDMA applications, oversimplifying fabric topology considerations, and lacking elastic service-level objectives (SLOs) guarantees-resulting in performance degradation and resource inefficiency. To bridge these gaps, we introduce Wiseswap, an elastic, datacenter network-aware DMS that delivers robust memory disaggregation for multi-tenant clouds through three key innovations: (1) Preemption-enabled isolation: A low-overhead kernel mechanism utilizes WAIT/ENABLE RDMA primitives to prioritize latency-critical swap operations over user-space RDMA flows, maintaining tenant fairness; (2) Adaptive fabric path selection: In-kernel telemetry dynamically probes latency and routes memory traffic through uncongested paths, mitigating interference from elephant flows; (3) Feedback-directed autoscaling: Fine-grained optimization of DMS-specific parameters-dynamically optimizes resource allocation under fluctuating workloads, guaranteeing stringent SLOs while minimizing resource overhead. |
Mingxuan Liu; Jianhua Gu; Tianhai Zhao; Dong Sun; |
| 414 | SuperEar: Eavesdropping on Mobile Voice Calls Via Stealthy Acoustic Metamaterials Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Zhiyuan Ning; Zhanyong Tang; Juan He; Weizhi Meng; Yuntian Chen; Jie Zhang; Zheng Wang; |
| 415 | SliceGX: Layer-wise GNN Explanation with Model-slicing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces SliceGX, a novel GNN explanation approach that generates explanations at specific GNN layers in a progressive manner. |
Cibo Yu; Tingting Zhu; Tingyang Chen; Yinghui Wu; Arijit Khan; Xiangyu Ke; |
| 416 | Robust Fake News Detection Using Large Language Models Under Adversarial Sentiment Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce AdSent, a sentiment-robust detection framework designed to ensure consistent veracity predictions across both original and sentiment-altered news articles. |
Sahar Tahmasebi; Eric M\{u}ller-Budack; Ralph Ewerth; |
| 417 | MCP Vs RAG Vs NLWeb Vs HTML: A Comparison of The Effectiveness and Efficiency of Different Agent Interfaces to The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet no systematic comparison of the effectiveness and efficiency of these interfaces on identical challenging task sets exists. To address this gap, we introduce a testbed consisting of four simulated e-shops, each offering its products via HTML, MCP, and NLWeb interfaces. |
Aaron Steiner; Ralph Peeters; Christian Bizer; |
| 418 | SAGE-RAI: Design Patterns for Transparent RAG Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents design patterns for building transparent RAG systems, derived from developing and deploying SAGE-RAI, an advanced multi-purpose RAG system, in an educational context. |
Joseph Kwarteng; Aisling Third; Alexander Mikroyannidis; David Tarrant; John Domingue; |
| 419 | VK-LSVD: A Large-Scale Industrial Dataset for Short-Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Short-video recommendation presents unique challenges, such as modeling rapid user interest shifts from implicit feedback, but progress is constrained by a lack of large-scale open datasets that reflect real-world platform dynamics. To bridge this gap, we introduce the VK Large Short-Video Dataset (VK-LSVD), the largest publicly available industrial dataset of its kind. |
Aleksandr Poslavsky; Alexander D’yakonov; Yuriy Dorn; Andrey Zimovnov; |
| 420 | DualDis: A Dual Disentanglement Network for Vehicle Re-identification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose DualDis, a novel vehicle re-identification framework that decouples identity-related features. |
Wenying He; Feiyu Wang; Guangquan Xu; Yude Bai; Fei Guo; |
| 421 | OpenDigger: A Practical Framework for Assessing Community Health and Sustainability in Open Source Collaboration Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present OpenDigger, a framework for multi-dimensional assessment of collaboration activities in OSEs. |
Wei Wang; Fanyu Han; Shengyu Zhao; Xuan Zhou; Weining Qian; Aoying Zhou; Xiaoya Xia; Liyun Yang; Rong Wang; Ning Jiang; Moming Duan; |
| 422 | Red-Teaming Privacy-Protective Perturbations: Blind Face Restoration As An Attack Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we theoretically analyze why Blind Face Restoration (BFR) algorithms are suited for red-teaming privacy-protective perturbations. |
Zelin Li; Yifan Liu; Huimin Zeng; Yaokun Liu; Ruichen Yao; Yang Zhang; Dong Wang; |
| 423 | Scaling Collaborative Filtering with Multimodal Contrastive Fine-tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present RecCLIP, a multimodal framework that reformulates user–item interactions as visual representations compatible with vision–language models(VLMs). |
Liwei Jin; Dan Luo; Lixin Zou; Chenliang Li; Xiangyang Luo; Xixun Lin; Liming Dong; Yifan Zhang; |
| 424 | Improving The Price of Anarchy Via Predictions in Parallel-Link Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study non-atomic congestion games on parallel-link networks with polynomial latencies. |
George Christodoulou; Vasilis Christoforidis; Alkmini Sgouritsa; Ioannis Vlachos; |
| 425 | ST-LEGO: Large Language Models As Modular Architects for Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Shuhao Li; Weidong Yang; Yue Cui; Lipeng Ma; Yixuan Li; Chaoteng Wu; Lu Qin; Fan Zhang; |
| 426 | MF3: Multimodal Federated Learning with Dual-Path Mamba-Transformer for Metro Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, building such predictive systems faces three key challenges: (1) the fragmentation of multimodal spatiotemporal data, (2) the inefficiency of existing models in capturing long-range dependencies, and (3) the data silos and privacy concerns inherent in distributed station infrastructures. To address these challenges, a multimodal federated learning framework named MF3 (Mamba-Transformer-Federated Metro Flow Prediction) is proposed. |
Bingjie Wang; Chao Zhang; Wentao Li; Deyu Li; |
| 427 | SGExplainer: Balanced Path-based Signed Graph Neural Network Explanation for Link Sign Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the gap, we propose SGExplainer, a novel method that leverages balanced paths, a concept rooted in signed graph theory, to provide clear and faithful explanations for LSP. |
Jie Gao; Jia Hu; Geyong Min; Fei Hao; |
| 428 | SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Transferable attacks in the text domain are still under-explored, with only a few studies addressing this challenging issue, often with suboptimal results due to equal treatment of submodels or inaccurate estimation of importance scores. To address these challenges, we propose a simple yet effective paradigm for transfer-based textual adversarial attack, named SEP-Attack. |
Han Liu; Zhi Xu; Xiaotong Zhang; Feng Zhang; Xiaoming Xu; Wei Wang; Fenglong Ma; Hong Yu; |
| 429 | MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present MetaKube, an experience-aware LLM framework through three synergistic innovations: (1) an Episodic Pattern Memory Network (EPMN) that abstracts diagnostic patterns from historical resolutions and provides confidence-calibrated retrieval for both rapid pattern matching and guided causal exploration, (2) a meta-cognitive controller that dynamically routes between intuitive and analytical pathways based on problem familiarity, optimizing the trade-off between speed and depth, and (3) KubeLLM, a locally-deployable 8B model enhanced through domain-specific post-training on our 7,000-sample Kubernetes Fault Resolution Dataset. |
Wei Sun; Ting Wang; Xinran Tian; Wanshun Lan; Xuhan Feng; Haoyue Li; Fangxin Wang; |
| 430 | EMSEdit: Efficient Multi-Step Meta-Learning-based Model Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, we find that MLME struggles in low-data regimes and incurs high training costs due to the use of KL divergence. To address these issues, we propose Efficient Multi-Step Edit (EMSEdit), which leverages multi-step backpropagation (MSBP) to effectively capture gradient-activation mapping patterns within editing samples, performs multi-step edits per sample to enhance editing performance under limited data, and introduces norm-based regularization to preserve unedited knowledge while improving training efficiency. |
Xiaopeng Li; Shasha Li; Xi Wang; Shezheng Song; Bin Ji; Shangwen Wang; Jun Ma; Xiaodong Liu; Mina Liu; Jie Yu; |
| 431 | AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, optimization-based methods lack the flexibility to adapt to dynamic market conditions, while RL-based approaches struggle to capture essential historical dependencies and observational patterns within the constraints of Markov Decision Process (MDP) frameworks. To address these limitations, we propose AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control. |
Xinxin Yang; Yangyang Tang; Yikun Zhou; Yaolei Liu; Yun Li; Bo Yang; |
| 432 | Mitigating Fine-tuning Bias: A Parameter-Efficient Debiasing Framework for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, targeting specific bias types limits their applications. To tackle these challenges, we propose PEDAL, a novel Parameter-Efficient DebiAsing framework for LLMs, which consists of three modules: Classifier, Modifier, and Reviewer. |
Qiuyu Li; Kun Zhang; Le Wu; Hao Liu; Hefei Xu; Xin Li; Si Wei; |
| 433 | Contextual Structure-Enhanced Selective Graph Convolutional Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although recent solutions have attempted to incorporate higher-order neighborhoods or reweighting schemes, they often inadvertently amplify structural noise by introducing a larger proportion of dissimilar nodes than similar ones, while simultaneously failing to capture nuanced contextual patterns due to their inability to discern subtle local structural variations across subgraphs. To holistically address these intractable and co-existing challenges, we propose the Contextual Structure Enhanced Selective Graph Convolutional Network (CSS-GCN), a novel architecture that organically synergizes contextual structure modeling with adaptive neighbor selection. |
Shifei Ding; Fangchen Li; Lili Guo; Jian Zhang; |
| 434 | WED-Net: A Weather-Effect Disentanglement Network with Causal Augmentation for Urban Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although recent methods adopt causal techniques to improve out-of-distribution generalization, they typically overlook temporal dynamics or depend on fixed confounder stratification. To address these limitations, we propose WED-Net (Weather-Effect Disentanglement Network), a dual-branch Transformer architecture that separates intrinsic and weather-induced traffic patterns via self- and cross-attention, enhanced with memory banks and fused through adaptive gating. |
Qian Hong; Siyuan Chang; Xiao Zhou; |
| 435 | NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall Via Evidence Chains Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Both categories lack a comprehensive understanding of retrieval quality optimization, resulting in a trade-off dilemma. To address these challenges, we propose comprehensive retrieval quality optimization criteria and introduce the NeocorRAG framework. |
Shiyao Peng; Qianhe Zheng; Zhuodi Hao; Zichen Tang; Rongjin Li; Qing Huang; Jiayu Huang; Jiacheng Liu; Yifan Zhu; Haihong E; |
| 436 | Reinforcement Learning-Constrained Segmented User Modeling with Large Language Models for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the direct application of LLMs to this task faces two major challenges: (1) limited context windows struggle to process the extensive user behavior sequences in real-world scenarios; (2) inherent hallucination effect can lead LLMs to infer spurious preferences that contradict true user intent, thereby degrading recommendation quality. To address this, we propose Rec 2, a Reinforcement learning-constrained segmented user modeling framework for recommendation. |
Yu Xia; Qing Tan; He Chen; Jingyu Chen; Qian Dong; |
| 437 | Balancing Privacy and Security of QNAME Minimisation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a quantitative model to assess both the privacy leakage and amplification potential across various QMIN strategies. |
Qinxin Li; Zhaohua Wang; Wenhao Wu; Zihan Li; Yiming Xia; Chuan Gao; Zhenyu Li; |
| 438 | CoLOR-DP: Conjugate Low-Rank Differential Privacy for Structure-Aware LoRA Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose Conjugate Low-Rank Differential Privacy (CoLOR-DP), a structure-aware mechanism that aligns DP with LoRA’s inherent geometry. |
Kai Zhang; Yuxuan Xu; Wenxiang Lin; Chaoqun Hong; Pei-Wei Tsai; Xin Yuan; Minhui Xue; |
| 439 | Enhancing Domain-Adaptive Hashing Via Evidential Learning and Progressive Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Junsheng Wang; Tiantian Gong; Yeyun Wu; Liyan Zhang; |
| 440 | Delayed Feedback Modeling for Post-Click Gross Merchandise Volume Prediction: Benchmark, Insights and Approaches Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our analysis and exploratory experiments on TRACE reveal two key insights: (1) the rapid evolution of the GMV label distribution necessitates modeling delayed feedback under online streaming training; (2) the label distribution of repurchase samples substantially differs from that of single-purchase samples, highlighting the need for separate modeling. Motivated by these findings, we propose RepurchasE-Aware Dual-branch prEdictoR (READER), a novel GMV modeling paradigm that selectively activates expert parameters according to repurchase predictions produced by a router. |
Xinyu Li; Sishuo Chen; Guipeng Xv; Li Zhang; Mingxuan Luo; Zhangming Chan; Xiang-Rong Sheng; Han Zhu; Jian Xu; Chen Lin; |
| 441 | Towards Foundation Models for MMKG: Multi-Task Inductive Generalization Via Task-Aware Routing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 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. |
Shundong Yang; Jing Yang; Xiaowen Jiang; Xiaofen Wang; Laurence T. Yang; Yuan Gao; Xinfa Jiang; Jie Chen; Chaojun Zhang; |
| 442 | Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents the first large-scale comparative study of bias in skin-toned emoji representations across two distinct model classes. |
Mingchen Li; Wajdi Aljedaani; Yingjie Liu; Navyasri Meka; Xuan Lu; Xinyue Ye; Junhua Ding; Yunhe Feng; |
| 443 | The Power of Penalties: Negativity-Aware Incentives for High-Quality Crowdsourced Data Labeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: High-quality data labeling is essential for training robust machine learning models; however, existing methods often ignore fraud or assume non-negative worker utility, failing to penalize harmful contributions without discouraging participation. To address this, we propose the Negativity-Aware Incentive (NAI) mechanism which introduces two novel components. |
Kai Wang; Runze Wu; Yu Xiong; Haifeng Sun; Anran Li; Shaojie Tang; Changjie Fan; Xiang-Yang Li; |
| 444 | When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While KG-RAG improves factual accuracy in complex tasks, existing KG-RAG models are often severely overconfident, producing high-confidence predictions even when retrieved sub-graphs are incomplete or unreliable, which raises concerns for deployment in high-stakes domains. To address this issue, we propose Ca2KG, a Causality-aware Calibration framework for KG-RAG. |
Jing Ren; Bowen Li; Ziqi Xu; Xikun Zhang; Haytham Fayek; Xiaodong Li; |
| 445 | PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose PruneRAG, a confidence-guided query decomposition framework that builds a structured query decomposition tree to perform stable and efficient reasoning. |
Shuguang Jiao; Xinyu Xiao; Yunfan Wei; Shuhan Qi; Chengkai Huang; Quan Z. Sheng; Lina Yao; |
| 446 | CFVBench: A Comprehensive Video Benchmark for Fine-grained Multimodal Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Using CFVBench, we systematically evaluate 7 retrieval methods and 14 widely-used MLLMs, revealing a critical bottleneck: current models (even GPT5 or Gemini) struggle to capture transient yet essential fine-grained multimodal details. To mitigate this, we propose Adaptive Visual Refinement (AVR), a plug-and-paly framework that adaptively increases frame sampling density and selectively invokes external tools when necessary. |
Kaiwen Wei; Xiao Liu; Jie Zhang; Zijian Wang; Ruida Liu; Yuming Yang; Xin Xiao; Xiao Sun; Haoyang Zeng; Changzai Pan; Yidan Zhang; Jiang Zhong; Peijin Wang; Yingchao Feng; |
| 447 | LHG: LLM-enhanced and Heterogeneous Graph-induced for Unsupervised Social Event Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite efforts, existing unsupervised SED models rely on graph structures to address the lack of textual content, resulting in unstable performance in dynamic social messages. To solve the above challenges, this work proposes an unsupervised SED framework with an LLM enhancement and Heterogeneous Graph induction (LHG). |
Zitai Qiu; Rongwei Xu; Congbo Ma; Shan Xue; Jian Yang; Guanfeng Liu; Quan Z. Sheng; Amin Beheshti; Jia Wu; |
| 448 | WPIS: From In-the-Wild Web Images to Physics-Aware 3D Scene Graphs for Physical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce WPIS (Web- and Physics-Informed Scene-understanding), a pipeline that compiles Physics-aware 3D Scene Graphs (P-3DSGs) from web imagery by fusing open-vocabulary instance/mask cues with relative geometry, augmenting nodes with real-valued liquid states and fine-grained hand–object interaction (HOI) subgraphs, and attaching concise natural-language functional relations—without intrinsics, multi-view, or CAD priors. |
Ke Ma; Cong Fu; Jianing Wang; Yifei Wang; Wenyuan Li; Xinggang Wang; Meng Wang; Tian Xia; |
| 449 | GPU-accelerated Multi-relational Parallel Graph Retrieval for Web-scale Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a GPU-accelerated Multi-relational Parallel Graph Retrieval (GMP-GR) framework to achieve effective yet efficient retrieval in web-scale recommendations. |
Zhuoning Guo; Guangxing Chen; Qian Gao; Xiaochao Liao; Jianjia Zheng; Lu Shen; Hao Liu; |
| 450 | Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches are confined to single-granularity (e.g., node-level or subgraph-level) during prompt generation, overlooking the inherently multi-scale structural information in graph data, which limits the diversity of prompt semantics. To address this issue, we pioneer the integration of multi-scale information into graph prompt and propose a Multi-Scale Graph Chain-of-Thought (MSGCOT) prompting framework. |
Ziyu Zheng; Yaming Yang; Ziyu Guan; Wei Zhao; Xinyan Huang; Weigang Lu; |
| 451 | Token-level Collaborative Alignment for LLM-based Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose Token-level Collaborative Alignment for Recommendation (TCA4Rec), a model-agnostic and plug-and-play framework that establishes an explicit optimization-level interface between CF supervision and LLM generation. |
Fake Lin; Binbin Hu; Zhi Zheng; Xi Zhu; Ziqi Liu; Zhiqiang Zhang; Jun Zhou; Tong Xu; |
| 452 | A Creator-Aware Recommendation System for Content Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To resolve the underlying supply-and-demand imbalance, we developed and deployed a creator-aware recommendation system to achieve three-player Stackelberg ecosystem equilibrium. |
Wenjing Li; Jiang Rong; Yao Hu; Zhenzhe Zheng; Fan Wu; |
| 453 | Re-Diffusion: Modeling Latent Residuals with Diffusion for Time-Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Generative latent diffusion models (LDMs) have been extensively applied in various fields yet underperform in time-series prediction. Therefore, We propose the Re-Diffusion model, a latent diffusion approach that generates backbone residuals specifically tailored for time-series forecasting. |
Boning Zhang; Haishuai Wang; Zehong Hu; Jiajun Wang; Hongyi Zhang; Jia Jia; |
| 454 | Communication-Efficient Federated Learning for Post-Flood Risk Assessment Using UAV Swarms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nonetheless, existing FL methods are plagued by the dilemma of excessive communication overhead and insufficient segmentation performance. Therefore, this paper proposes a Communication-Efficient Federated Distillation (CEFD) framework. |
Yongkang Zhao; Hailin Feng; Tingting Wang; Thippa Reddy Gadekallu; Kai Fang; Wei Wang; |
| 455 | Towards Fair Large Language Model-based Recommender Systems Without Costly Retraining Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: 2) Debiasing methods relying on retraining are computationally infeasible given the massive parameter scale of LLMs. To overcome these challenges, we propose FUDLR (Fast Unified Debiasing for LLM-RS). |
Jin Li; Huilin Gu; Shoujin Wang; Qi Zhang; Shui Yu; Chen Wang; Xiwei Xu; Fang Chen; |
| 456 | DSTAG: A Semantic Tag-Enhanced Dual-Graph Convolutional Network for Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods predominantly focus on modeling sequential and structural dependencies, often overlooking the rich semantic information embedded in entities and relations, as well as the higher-order interactions among them, which limits their ability to handle complex, evolving scenarios effectively. To address these limitations, we propose DSTAG, a novel TKGC approach based on a semantic tag-enhanced dual-graph convolutional network. |
Yuchao Zhang; Xiangjie Kong; Kailun Ye; Shangfei Zheng; Guojiang Shen; |
| 457 | Few-shot and Zero-shot Audience Expansion with User and Task Model Pre-training on Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces UTMP, a multi-stage pre-training framework for few-shot and zero-shot audience expansion on tabular data. |
Siwei Qiang; Pengyu Li; Zhe Yu; Mengyao Sun; Jun Chen; Bo Zheng; |
| 458 | Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While effective, these approaches often incur high computational costs, limiting their applicability in large-scale industrial systems with long user sequences. To address this challenge, we propose the Transition-Aware Graph Attention Network (TGA), a linear-complexity approach for modeling multi-behavior transitions. |
Hanqi Jin; Gaoming Yang; Zhangming Chan; Yapeng Yuan; Longbin Li; Fei Sun; Yeqiu Yang; Jian Wu; Yuning Jiang; Bo Zheng; |
| 459 | Camel: Frame-Level Bandwidth Estimation for Low-Latency Live Streaming Under Video Bitrate Undershooting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: When a high-bitrate frame is suddenly produced, sending at the wrong rate can either trigger packet loss or increase queueing delay, resulting in playback stalls. To address these issues, we present Camel, a novel frame-level congestion control algorithm (CCA) tailored for LLS. |
Liming Liu; Zhidong Jia; Li Jiang; Wei Zhang; Lan Xie; Feng Qian; Leju Yan; Bing Yan; Qiang Ma; Zhou Sha; Wei Yang; Yixuan Ban; Xinggong Zhang; |
| 460 | Diffusion-based Kriging Model with Graph-enhanced Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Due to limitations such as reliance on static graph structures and iterative Graph Convolution Network (GCN) frameworks, accurate kriging remains challenging. To address these issues, we propose a Diffusion-based Kriging Model with Graph-enhanced Attention (DKM-GA). |
Mingtao Zhang; Guoli Yang; Zhanxing Zhu; Guangyin Jin; Mengzhu Wang; Xiaoying Bai; |
| 461 | Learning to Evolve: Bayesian-Guided Continual Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel CKGE framework, BAKE. |
Linyu Li; Zhi Jin; Yuanpeng He; Dongming Jin; Yichi Zhang; Haoran Duan; Xuan Zhang; Zhengwei Tao; Tashi Nyima; |
| 462 | Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel model named SharpReCL for imbalanced text classification tasks. |
Mengyu Li; Yonghao Liu; Fausto Giunchiglia; Ximing Li; Xiaoyue Feng; Renchu Guan; |
| 463 | Scalable and Provable Biclique-Preserving Clustering: The Power of Counting-based Approaches Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Additionally, existing methods are overly dependent on biclique enumeration, resulting in poor scalability. To address these challenges, we propose ECRC, a simple yet provable Edge-Centric Reweighting Clustering framework that provides strict approximation guarantees for any biclique. |
Longlong Lin; Zeli Wang; Rong-Hua Li; Xiaohai Dai; Li Ni; Jin Zhao; |
| 464 | All-but-one MMS Allocation for Chores Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study the problem of fairly allocating m indivisible chores among n agents with additive cost functions. |
Jiawei Qiu; Xiaowei Wu; Cong Zhang; Shengwei Zhou; |
| 465 | VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches face two critical challenges: (1) most models suffer from exponentially increasing computational complexity, making it difficult to scale to large graphs; (2) attention mechanisms based on node-level operations limit the flexibility of the model and result in poor generalization performance in out-of-distribution (OOD) scenarios. To address these issues, we propose VecFormer (the Vec tor Quantized Graph Transformer ), an efficient and highly generalizable model for node classification, particularly under OOD settings. |
Jingbo Zhou; Jun Xia; Siyuan Li; Yunfan Liu; Wenjun Wang; Yufei Huang; Changxi Chi; Mutian Hong; Zhuoli Ouyang; Shu Wang; Zhongqi Wang; Xingyu Wu; Chang Yu; Stan Z. Li; |
| 466 | S²KT:Modeling Uncertainty in Knowledge Tracing Via Semantic-aware Structured Gaussian Distributions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To model fine-grained knowledge transfer, we introduce a semantic propagation mechanism based on the Bhattacharyya distance to diffuse mastery updates within the latent space. |
Wenhui Wu; Tiancheng Zhang; Hengyu Liu; Lun Du; Zikai Li; Mingxing Shao; Minghe Yu; Yifang Yin; Ge Yu; |
| 467 | Cross-Platform Narrative Prediction: Leveraging Platform-Invariant Discourse Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we frame cross-platform prediction as a network proximity problem: rather than tracking individual users across platforms or relying on brittle signals like shared URLs or hashtags, we construct platform-invariant discourse networks that link users through shared narrative engagement. |
Patrick Gerard; Luca Luceri; Leonardo Blas; Emilio Ferrara; |
| 468 | The Asymmetric Vulnerability: Bypassing LLM Defenses Via Guardrail-Model Mismatch Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through systematic analysis, we show that guardrails are fragile when facing minor character perturbations, while LLMs remain semantically resilient and can still reconstruct malicious intent from noisy inputs. Exploiting this asymmetry, we propose RepMism, a hybrid adversarial framework that combines character injection with chain-of-thought hijacking, coordinated through hierarchical scheduling and safety continuation. |
Junyi Wang; Zhibin Zhu; Chuanyi Liu; |
| 469 | PWAVEP: Purifying Imperceptible Adversarial Perturbations in 3D Point Clouds Via Spectral Graph Wavelets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these threats, in this paper, we propose a plug-and-play and non-invasive defense mechanism in the spectral domain, grounded in a theoretical and empirical analysis of the relationship between imperceptible perturbations and high-frequency spectral components. Building upon these insights, we introduce a novel purification framework, termed PWAVEP, which begins by computing a spectral graph wavelet domain saliency score and local sparsity score for each point. |
Haoran Li; Renyang Liu; Hongjia Liu; Chen Wang; Long Yin; Jian Xu; |
| 470 | Graph Adversarial Defense Via Hilbert-Schmidt Independence Criterion Against Influence Maximization Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This may leave a potential risk in real-world applications. To address this issue, we propose a Graph Adversarial Defense method based on the Hilbert-Schmidt Independence Criterion (HSIC-GAD). |
Yuxing Guo; Jianqing Liang; Kaixuan Yao; Zhihao Guo; Jiye Liang; |
| 471 | ScaleGNN: Towards Scalable Graph Neural Networks Via Adaptive High-order Neighboring Feature Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To tackle these, we propose ScaleGNN, which adaptively fuses multi-hop node features for scalable and effective graph learning. |
Xiang Li; Jianpeng Qi; Haobing Liu; Yuan Cao; Guoqing Chao; Zhongying Zhao; Junyu Dong; Xinwang Liu; Yanwei Yu; |
| 472 | Concept Relationship Embedding-Based Interactive Web Application for Explainable Medical Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This oversimplified paradigm ignores the rich relationships among concepts and their causal influence on disease outcomes. To overcome these limitations, we propose the Concept Relationship Embedding Model (CREM) for interpretable medical diagnosis. |
Lei Zhao; Xingguo Lv; Qika Lin; Kaize Shi; Xiaoming Qi; Bin Pu; Kenli Li; |
| 473 | Accurate Trajectory Recovery in Underserved Areas Via Location Inference from Web Crowdsourced Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we propose Region-aware Hierarchical Trajectory Recovery (RHTR) model, designed for location inference from web crowdsourced data in sparse, roadless scenarios. |
Tangwei Ye; Liang Hu; Zhongyuan Lai; Qi Zhang; Yiming Wu; Jiaxing Miao; Yijun Yang; Kun Yi; |
| 474 | KMLP: A Scalable Hybrid Architecture for Web-Scale Tabular Data Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The inherent complexities of these features—characterized by anisotropy, heavy-tailed distributions, and non-stationarity—not only impose bottlenecks on the training efficiency and scalability of mainstream models like Gradient Boosting Decision Trees (GBDTs), but also compel practitioners into laborious, inefficient, and expert-dependent manual feature engineering. To systematically address this challenge, we introduce KMLP, a novel hybrid deep architecture. |
Mingming Zhang; Pengfei Shi; Junbo Zhao; Ningtao Wang; Feng Zhao; Guandong Sun; Yulin Kang; Xing Fu; Zhiqing Xiao; Weiqiang Wang; Ruizhe Gao; |
| 475 | Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, effectively incorporating multimodal item features into FR remains an open challenge, due to efficiency constraints, distribution heterogeneity, and feature utilization alignment with the recommendation objective. To tackle these issues, we propose GFMFR, a novel multimodal fusion framework for federated recommendation. |
Chunxu Zhang; Weipeng Zhang; Guodong Long; Zhiheng Xue; Riting Xia; Bo Yang; |
| 476 | Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose FCUCR, a federated continual recommendation framework designed to support long-term personalization in a privacy-preserving manner. |
Chunxu Zhang; Zhiheng Xue; Guodong Long; Weipeng Zhang; Bo Yang; |
| 477 | BHGap: A Deep Iterative Prompting and Multi-stage Alignment Framework for Dynamic Facial Expression Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches are generally constrained by two major challenges: (1) shallow and static fusion mechanisms, which fail to capture the dynamic co-evolution of audio-visual features during deep interaction; (2) implicit and coarse alignment strategies, which are insufficient to bridge the modality gap caused by heterogeneous feature distributions. To address these issues, we propose a novel framework, BHGap, which integrates deep iterative prompt generation with multi-stage feature alignment and fusion. |
Yichi Zhang; Yunqi Han; Jiayue Ding; Liangyu Chen; |
| 478 | Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we achieve the best of both worlds through the lens of Riemannian geometry, which provides the potential to adjust the message passing behavior in different regions. |
Li Sun; Ming Zhang; Wenxin Jin; Zhongtian Sun; Zhenhao Huang; Hao Peng; Sen Su; Philip Yu; |
| 479 | K&L: Penetrating Backdoor Defense with Key and Locks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we identify three key requirements that a backdoor attack must satisfy to be successful. |
Xinyi Wang; Jiayu Zhang; Zhiyu Zhu; Zhibo Jin; Dong Yuan; Huaming Chen; |
| 480 | Towards Efficient and Interpretable Medical Concept Representation Via Ontology-driven Residual Vector Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose MedRQ, an ontology-driven residual vector quantization framework that aligns discrete codes with multi-level clinical ontologies. |
Hang Lv; Kaisong Zhang; Yanchao Tan; Xing Chen; |
| 481 | Unveiling and Simulating Short-Video Addiction Behaviors Via Economic Addiction Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our analysis shows that short-video addiction follows functional patterns similar to traditional forms of addictive behavior (e.g., substance abuse) and that its intensity is consistent with findings from previous social science studies. To develop a simulator that can learn and model these patterns, we introduce a novel training framework, AddictSim. |
Chen Xu; Zhipeng Yi; Ruizi Wang; Wenjie Wang; Jun Xu; Maarten de Rijke; |
| 482 | Understanding The Consequences of VTuber Reincarnation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Understanding the quantitative fallout of reincarnation is crucial for mitigating this damage and fostering a more sustainable industry. To address this gap, we conduct the first large-scale empirical study of VTuber reincarnation, analyzing 12 significant cases using a comprehensive dataset of 728K livestream sessions and 4.5B viewer interaction records. |
Yiluo Wei; Gareth Tyson; |
| 483 | TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a structure-disentangled multiscale generation framework for time series. |
Xiangyu Xu; Qingsong Zhong; Jilin Hu; |
| 484 | VisionST: Coordinating Cross-modal Traffic Prediction with Interactive Geo-image Encoding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Web-sourced geo-images, e.g., satellite imagery, encompass comprehensive contextual information and offer an effective way to represent diverse modalities. To unleash the power of such geo-images, we propose VisionST, a Vision-augmented Spatial-Temporal Neural Network, which coordinates cross-modal traffic prediction with interactive geo-image encoding. |
Jinwen Chen; Hao Miao; Chenxi Liu; Yan Zhao; Kai Zheng; |
| 485 | Pattern-aware Illicit Account Detection Based on User Behavior Sequences Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This task presents two key challenges: 1) illicit accounts often mimic benign users by performing normal-looking behavior subsequences, and 2) behaviors with the same action (e.g., add-friend, initiate-transaction) can serve different purposes, such as illicit or benign. To address these challenges, we propose a Pattern-aware Illicit Accounts Detection (PIAD) framework that consists of three components: 1) a dual-perspective pattern mining module that extracts category-specific self- and interaction-behavior patterns from behavior sequences to capture distinct behavioral regularities across different user types; 2) a contextualized action semantic encoding algorithm that aligns action codings with contextual dependencies among behaviors within user sequences to capture variations in purposes when behaviors with the same actions occur under different contexts; and 3) a pattern-aware fusion model that integrates the mined patterns with the context and interaction in behavior sequences to learn discriminative representations for detection. |
Zehao Wang; Lanjun Wang; Fuxia Guo; Yanjie Dong; |
| 486 | MultiHateLoc: Towards Temporal Localisation of Multimodal Hate Content in Online Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This challenge is even more noticeable under weak supervision, where only video-level labels are available, and static fusion or classification-based architectures struggle to capture cross-modal and temporal dynamics. To address these challenges, we propose MultiHateLoc, the first framework designed for weakly-supervised multimodal hate localisation. |
Qiyue Sun; Tailin Chen; Yinghui Zhang; Yuchen Zhang; Jiangbei Yue; Jianbo Jiao; Zeyu Fu; |
| 487 | ReaLM: Residual Quantization Bridges Knowledge Graph Embeddings and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This discrepancy hinders effective semantic transfer and limits their performance. To address this challenge, we propose ReaLM, a novel and effective framework that bridges the gap between KG embeddings and LLM tokenization through the mechanism of residual vector quantization. |
Wenbin Guo; Xin Wang; Jiaoyan Chen; Lingbing Guo; Zhao Li; Zirui Chen; |
| 488 | Matrix As Plan: Structured Logical Reasoning with Feedback-Driven Replanning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To further enhance the logical reasoning capabilities of LLMs, we propose MatrixCoT, a structured CoT framework with a matrix-based plan. |
Ke Chen; Jiandian Zeng; Zihao Peng; Guo Li; Guangxue Zhang; Tian Wang; |
| 489 | How Human Experts Educate Specialized LLMs: Filling Knowledge Gaps in KG-Augmented Generation Through Hallucination Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet, these methods face two key limitations: 1) heavy reliance on costly expert knowledge, and 2) neglect of the connection between the LLM’s inherent knowledge and external expert knowledge. To address these issues, this paper introduces Epistemic Cognition-enhanced Specialized LLMs (EC-spLLM), a novel evolutionary framework that instills epistemic cognition into the LLM to systematically exploit both its internal knowledge and expert knowledge. |
Chaofan Li; Lixing Chen; Junhua Tang; Yang Bai; Yutong Zhang; Zhi Zheng; Pan Zhou; Zhe Qu; |
| 490 | BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although there exist many methods employing efficient attention and SSM-based models, these approaches struggle to effectively model long sequences and may exhibit unstable performance on short sequences. To address these challenges, we design a sparse attention mechanism, BlossomRec, which models both long-term and short-term user interests through attention computation to achieve stable performance across sequences of varying lengths. |
Mengyang Ma; Xiaopeng Li; Wanyu Wang; Zhaocheng Du; Jingtong Gao; Pengyue Jia; Yuyang Ye; Yiqi Wang; Yunpeng Weng; Weihong Luo; Xiao Han; Xiangyu Zhao; |
| 491 | Fair and Carbon-Aware LLM Routing for Web Services Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a carbon-aware and fairness-aware routing framework for LLM-based network services, which jointly optimizes energy consumption, output quality, and distributional fairness. |
Tingting Li; Ziming Zhao; Zhaoxuan Li; Xiaofei Yue; Jiongchi Yu; |
| 492 | SCOUT: Structure-Aware Aspect and Anchor-Count Selection for Node Attribute Augmentation Via Positional Information Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Subsequently, leveraging the heavy-tailed distribution typically observed in node centrality, SCOUT utilizes an elbow detection method on the ranked centrality curve to adaptively determine the K most representative nodes as anchors. |
Dong-Hyuk Seo; Sein Kim; Taeri Kim; Won-Yong Shin; Sang-Wook Kim; |
| 493 | Not All Information Brings Benefits: Personalization-Driven Agent Debate for Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This ultimately degrades the performance of conversational recommendations. To address this issue, inspired by the behavioral decision theory, this paper proposes a novel model, Personalization-Driven Agent Debate for Conversational Recommendation, named EyeCRS. |
Pengfei Zhang; Guojia An; Jin Huang; Yuhan Yang; Yang Yang; Jie Zou; |
| 494 | Thinking Broad, Acting Fast: Latent Reasoning Distillation from Multi-Perspective Chain-of-Thought for E-Commerce Relevance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, two key limitations remain: (1) most existing approaches rely on single-perspective CoT reasoning, which fails to capture the multifaceted nature of e-commerce relevance (e.g., user intent vs. attribute-level matching vs. business-specific rules); and (2) although CoT-enhanced LLMs offer rich reasoning capabilities, their high inference latency necessitates knowledge distillation for real-time deployment, yet current distillation methods discard the CoT rationale structure at inference, using it only as a transient auxiliary signal and thereby forfeiting its reasoning utility for online serving. To address these challenges, we propose a novel framework that better exploits CoT semantics throughout the optimization pipeline. |
Baopu Qiu; Hao Chen; Yuanrong Wu; Changtong Zan; Chao Wei; Weiru Zhang; Xiaoyi Zeng; |
| 495 | MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we extend the VQA dataset to create DD-VQA+, which features a richer set of attributes and a more diverse range of samples. |
Tao Chen; Jingyi Zhang; Decheng Liu; Chunlei Peng; |
| 496 | Tabular Foundation Models Are Strong Graph Anomaly Detectors Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose TFM4GAD, a simple yet effective framework that adapts tabular foundation models (TFMs) for graph anomaly detection. |
Yunhui Liu; Tieke He; Yongchao Liu; Can Yi; Hong Jin; Chuntao Hong; |
| 497 | Deterring A Small Collusion Is All You Need Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Transaction Fee Mechanisms (TFMs) study auction design in the Blockchain context, and emphasize robustness against miner and user collusion, moreso than traditional auction theory. |
Yotam Gafni; |
| 498 | Mitigating Dynamic Graph Distribution Shifts Via Mixture of Variational Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our analysis reveals that environment-specific factors misguide the learning process and lead to unsatisfactory out-of-distribution (OOD) generalization. Based on this insight, we propose MoVE, a Mixture of Variational Experts network to mitigate complex distribution shifts in dynamic graphs. |
Qianyu Song; Chao Li; Yeyu Yan; Hui Zhou; Zhongying Zhao; Qingtian Zeng; |
| 499 | Causality Enhancement for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet this direction has been rarely explored, as identifying unbiased real causal labels is highly challenging in real-world scenarios. In this work, we attempt to take a first step in this direction by proposing a causality-enhanced framework, named CE-CDR. |
Zhibo Wu; Yunfan Wu; Lin Jiang; Ping Yang; Yao Hu; |
| 500 | A Generative Contextual Comprehension Paradigm for Takeout Ranking Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This challenge is further amplified in location-based services such as food delivery, where user decisions are shaped by dynamic spatial, temporal, and individual contexts. To address these limitations, we propose a novel generative framework that reframes ranking as a context comprehension task, modeling heterogeneous signals in a unified architecture. |
Ziheng Ni; Congcong Liu; Cai Shang; Yiming Sun; Junjie Li; Zhiwei Fang; Guangpeng Chen; Li Jian; Zehua Zhang; Changping Peng; Zhangang Lin; Ching Law; Jingping Shao; |
This table only includes 500 papers selected by our daily digest algorithm. To continue with the full list (~950 papers), please visit Paper Digest: WWW-2026 (Full List).