OpenRaiser/Intern-Atlas huggingface dataset:
TCPN: Temporal Pyramidal Recurrent Network with Contrastive Learning for Temporal Knowledge Graph Reasoning
Authors: Liu Yang, Zixuan Luo, Tingxuan Chen, Zidong Wang, Limin Liu
Venue: CIKM
Year: 2025

Temporal Knowledge Graphs (TKGs) serve as crucial tools for representing dynamic changes in the real world. Extrapolation reasoning within TKGs aims to predict entirely unknown future facts based on limited historical data, offering considerable practical value across various fields. However, existing methods generally focus on the recurrence and periodicity of historical facts, while overlooking the dynamic interactions associated with future facts. Moreover, these methods fail to capture historical evolutionary patterns, which grow increasingly complex as historical data accumulates. To this end, we propose TCPN, a novel Temporal Pyramidal Recurrent Network with contrastive learning for TKG extrapolation reasoning. Specifically, TCPN leverages a temporal pyramidal recurrent network to capture historical dependencies across multiple temporal scales, thereby refining temporal feature representations over extended time spans. Furthermore, TCPN seamlessly integrates contrastive learning to effectively align historical information with query semantics relevant to future facts. Lastly, we incorporate an adaptive time-aware mechanism, which uniformly models long-short term dependencies in time series with different granularities, explicitly fusing temporal feature information. Extensive experiments on four widely used TKG datasets show that TCPN significantly outperforms state-of-the-art methods across all metrics.
Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations
Authors: Bing Wang, Ximing Li, Yiming Wang, Changchun Li, Jiaxu Cui, Renchu Guan, Bo Yang
Venue: CIKM
Year: 2025

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.
Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling
Authors: Yang Cao, Bo Chen, Xiaoyu Li, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Mingda Wan
Venue: CIKM
Year: 2025

This paper introduces Force Matching (ForM), a novel framework for generative modeling that represents an initial exploration into leveraging special relativistic mechanics to enhance the stability of the sampling process. By incorporating the Lorentz factor, ForM imposes a velocity constraint, ensuring that sample velocities remain bounded within a constant limit. This constraint serves as a fundamental mechanism for stabilizing the generative dynamics, leading to a more robust and controlled sampling process. We provide a rigorous theoretical analysis demonstrating that the velocity constraint is preserved throughout the sampling procedure within the ForM framework. To validate the effectiveness of our approach, we conduct extensive empirical evaluations. On the half-moons dataset, ForM significantly outperforms baseline methods, achieving the lowest Euclidean distance loss of 0.714, in contrast to vanilla first-order flow matching (5.853) and first- and second-order flow matching (5.793). Additionally, we perform an ablation study to further investigate the impact of our velocity constraint, reaffirming the superiority of ForM in stabilizing the generative process. The theoretical guarantees and empirical results underscore the potential of integrating special relativity principles into generative modeling. Our findings suggest that ForM provides a promising pathway toward achieving stable, efficient, and flexible generative processes. This work lays the foundation for future advancements in high-dimensional generative modeling, opening new avenues for the application of physical principles in machine learning.
FinS-Pilot: A Benchmark for Online Financial RAG System
Authors: Feng Wang, Yiding Sun, Jiaxin Mao, Xue Wei, Danqing Xu
Venue: CIKM
Year: 2025

Large language models (LLMs) have demonstrated remarkable capabilities across various professional domains, with their performance typically evaluated through standardized benchmarks. In the financial field, the stringent demands for professional accuracy and real-time data processing often necessitate the use of retrieval-augmented generation (RAG) techniques. However, the development of financial RAG benchmarks has been constrained by data confidentiality issues and the lack of dynamic data integration. To address this issue, we introduce FinS-Pilot, a novel benchmark for evaluating RAG systems in online financial applications. Constructed from real-world financial assistant interactions, our benchmark incorporates both real-time API data and text data, organized through an intent classification framework covering critical financial domains. The benchmark enables comprehensive evaluation of financial assistants' capabilities in handling both static knowledge and time-sensitive market information.Through systematic experiments with multiple Chinese leading LLMs, we demonstrate FinS-Pilot's effectiveness in identifying models suitable for financial applications while addressing the current gap in specialized evaluation tools for the financial domain. Our work contributes both a practical evaluation framework and a curated dataset to advance research in financial NLP systems. The code and dataset are accessible on GitHub.
CCAgent: Coordinating Collaborative Data Scaling for Operating System Agents via Web3
Authors: Liang Chen, Haozhe Zhao, Yinzhen Huang, Yang Luo, Tsekai Lin, Weichu Xie, Ruoyu Wu, Peiyi Wang, Runxin Xu, Ming Wu, Baobao Chang
Venue: CIKM
Year: 2025

The current AI revolution, fueled by Large Language Models (LLMs), heavily relies on vast open-access internet data. However, the Operating System (OS) Agent field faces a significant data sparsity challenge due to the lack of public data collection systems and privacy concerns. To address this, we introduce CCAgent Net, a system that coordinates and incentivizes internet users to contribute to scaling OS agent datasets. Furthermore, we propose GUI-Pipe, an automated data post-processing pipeline that evaluates, filters, and transforms raw user-uploaded OS data into trainable image-instruction-answer data. This process results in CCAgent-Instruct, the largest instruction-based multi-platform GUI agent dataset. Our experiments demonstrate CCAgent's effectiveness in advancing OS Agent development. For instance, our CCAgent-GUI-3B model achieves a score of 33.7 (+127%) on the challenging out-of-domain Screenspots-Pro benchmark, significantly outperforming other advanced open-source models like UI-TARS-2B, Qwen2.5-VL-3B/7B, and UGround-V1-7B, even those of larger sizes. Our experiments also reveal the scaling behaviors of GUI Agent training and insights for future direction.
Empirical Analysis on User Profile in Personalized LLMs
Authors: Bin Wu, Zhengyan Shi, Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz
Venue: CIKM
Year: 2025

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance performance on a wide range of tasks. However, the precise role of user profiles and their effect mechanism on LLMs is unclear. This study first confirms that the effectiveness of user profiles stems primarily from their personalization information, with input-relevant information contributing meaningfully only when built upon personalization. Furthermore, we investigate how user profiles affect the personalization of LLMs. Within the user profile, we reveal that it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. This discovery unlocks the potential of LLMs to incorporate more user profiles within the constraints of limited input length. As for the position of user profiles, we observe that user profiles integrated into different positions of the input context do not contribute equally to personalization. Instead, user profiles closer to the beginning have more impact on the personalization of LLMs. Our findings reveal the role of user profiles for the personalization of LLMs, and showcase how incorporating user profiles impacts performance to leverage user profiles effectively.
A Cost-Aware Approach for Collaborating Large Language Models and Small Language Models
Authors: Zheng Li, Xuyun Zhang, Sheng Lu, Hua Deng, Hao Tian, Wanchun Dou
Venue: CIKM
Year: 2025

The emerging reasoning ability of large language models (LLMs) and accompanying commercial applications offer a promising path for service providers to deploy intelligent agents on their own products through API calls. However, the black-box nature of LLMs has driven providers to try prompt tuning to improve reasoning quality for competitiveness, while the generated reasoning logic results in additional service costs. Although some works have proposed collaborating LLMs and Small Language Models (SLMs) to reduce the frequency of LLM calls, most overlook the actual number of tokens interacting with the LLMs, which results in a potentially high cost still. Furthermore, directly compressing the prompt to reduce tokens often leads to a significant accuracy loss. To address the above challenges, we propose a cost-aware approach for collaborating LLMs and SLMs, named Coco. In our method, a confidence-based task assignment method is designed which leverages the result confidence of SLMs to assess task complexity and determine whether LLM involvement is necessary. For complex tasks, the SLM adapts the input by compressing unnecessary information according to confidence. Considering the potential loss of accuracy, prompt tuning-based reasoning optimization methods are introduced to guide the LLM in generating both the reasoning logic sketch and the final result. Finally, logic alignment is applied to fuse sketches from both models, ensuring the rationality of the reasoning logic. Experimental results on three open-source datasets demonstrate that our approach effectively reduces the cost of API calls to LLMs while ensuring the reasoning accuracy and the reasonableness of generated logic.
Harnessing Commonsense: LLM-Driven Knowledge Integration for Fine-Grained Sentiment Analysis
Authors: Kai Zhang, Yupeng Han
Venue: CIKM
Year: 2025

Fine-grained sentiment analysis, which aims to identify sentiments associated with specific aspects within sentences, faces challenges in effectively incorporating commonsense knowledge. Recent advancements leveraging large language models (LLMs) as data generators show promise but are limited by the LLMs' lack of nuanced, domain-specific understanding and pose a significant risk of data leakage during inference, potentially leading to inflated performance metrics. To address these limitations, we propose LLM-Kit, a novel framework for commonsense-enhanced fine-grained sentiment analysis that integrates knowledge via LLM-guided graph construction, effectively mitigating data leakage risks. LLM-Kit operates in two key stages: (1) Commonsense Graph Construction (CGC): We design second-order rules and leverage LLMs for evaluation to ensure the accuracy of the generated graph and mitigate the risk of data leakage from LLMs. (2) Knowledge-integration Graph Representation Learning (KGRL): We extract knowledge that is aware of various aspects through Graph Representation Learning (GRL). To capture the underlying semantic nuances within the input sentence, we develop a Sentence Semantic Learning (SSL) module based on RoBERTa that explicitly encodes internal semantics. This module provides complementary information to the GCN, improving the model's ability to discern subtle sentiment variations related to different aspects. Comprehensive experiments on three public datasets affirm that LLM-Kit achieves comparable performance with state-of-the-art models.
Social Relation Meets Recommendation: Augmentation and Alignment
Authors: Lin Wang, Weisong Wang, Xuanji Xiao, Qing Li
Venue: CIKM
Year: 2025

Recommender systems are essential for modern content platforms, yet traditional behavior-based models often struggle with cold users who have limited interaction data. Engaging these users is crucial for platform growth. To bridge this gap, we propose leveraging the social-relation graph to enrich interest representations from behavior-based models. However, extracting value from social graphs is challenging due to relation noise and cross-domain inconsistency. To address the noise propagation and obtain accurate social interest, we employ a dual-view denoising strategy, employing low-rank SVD to the user-item interaction matrix for a denoised social graph and contrastive learning to align the original and reconstructed social graphs. Addressing the interest inconsistency between social and behavioral interests, we adopt a ''mutual distillation'' technique to isolate the original interests into aligned social/behavior interests and social/behavior specific interests, maximizing the utility of both. Experimental results on widely adopted industry datasets verify the method's effectiveness, particularly for cold users, offering a fresh perspective for future research. The implementation can be accessed at https://github.com/WANGLin0126/CLSRec.
Audience-Aware and Self-Adaptive Multi-Interest Modeling for Sharing Rate Prediction in Affiliate Marketing
Authors: Zhe Wang, Ziyu Guan, Yujian Cao, Yaming Yang, Rui Wang, Bin Tong, Wei Zhao, Hongbo Deng
Venue: CIKM
Year: 2025

Affiliate marketing, a component of modern digital marketing, leverages partnerships among merchants, promoters, and consumers to enhance item visibility and drive sales. Promoters act as critical intermediaries, sharing items with their communities to promote items while earning commissions. Accurate prediction of the sharing rate of promoters enables platforms to optimize recommendation performance, thereby improving promotional efficiency. However, existing related methods are mainly designed for consumer-oriented scenarios (C-end), and face significant limitations in modeling the promoters (B-end), which are typically characterized by audience group attachment. Specifically, three core challenges emerge: (1) how to organically integrate audience preferences while maintaining promoter dominance, (2) how to accommodate promoters' diverse interest scopes, and (3) how to capture the complex one-to-many relationships between promoters and their audiences. For Challenge (1), we employ a dynamic routing mechanism based on interest capsules to model the diverse interests of promoters, where audience groups are used to optimize the interest routing via a novel dual-channel attention mechanism, thus allowing audience groups to explicitly participate in the promoter decision-making process with an auxiliary role. For Challenge (2), a parameter-free, confidence-aware interest activation mechanism is introduced to adaptively select sparse interest capsules. For Challenge (3), we pioneer the use of hypergraphs in CTR prediction to model one-to-many relationships between promoters and audiences. Extensive experiments are conducted on two real-world datasets to validate the effectiveness of our approach. Furthermore, the model is deployed on the Alimama platform, which hosts over 100,000 promoters. Online A/B testing results demonstrate that our method achieves a 5.31% average improvement over online baselines.
Transferable Deep Clustering Model
Authors: Zheng Zhang, Liang Zhao
Venue: CIKM
Year: 2025

Deep learning has shown remarkable success in the field of clustering recently. However, how to transfer a trained clustering model on a source domain to a target domain by leveraging the acquired knowledge to guide the clustering process remains challenging. Existing deep clustering methods often lack generalizability to new domains because they typically learn a group of fixed cluster centroids, which may not be optimal for the new domain distributions. In this paper, we propose a novel transferable deep clustering model that can automatically adapt the cluster centroids according to the distribution of data samples. Rather than learning a fixed set of centroids, our approach introduces a novel attention-based module that can adapt the centroids by measuring their relationship with samples. In addition, we theoretically show that our model is strictly more powerful than some classical clustering algorithms such as k-means or Gaussian Mixture Model (GMM). Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of our proposed transfer learning framework, which significantly improves the performance on target domain and reduces the computational cost.
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
Authors: Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, Liang Zhao
Venue: CIKM
Year: 2025

Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios. Despite the potential for deeper insights, existing TAG representation learning primarily omit the semantic relationship among node texts, and mostly relies on supervised methods, necessitating extensive labeled data and limiting applicability across diverse contexts. This paper introduces a new self-supervised learning framework, Text-Attributed-Graph Multi-View Alignment (TAGA), which overcomes these constraints by integrating TAGs' structural and semantic dimensions. TAGA constructs two complementary views: Text-of-Graph view, which organizes node texts into structured documents based on graph topology, and the Graph-of-Text view, which converts textual nodes and connections into graph data. By aligning representations from both views, TAGA captures joint textual and structural information. In addition, a novel structure-preserving random walk algorithm is proposed for efficient training on large-sized TAGs. Our framework demonstrates strong performance in zero-shot and few-shot scenarios across eight real-world datasets.
Revisiting Trajectories to Road: A New Diffusion Model and A New Dataset with 1, 000, 000, 000 Points
Authors: Yang Wang, Miaomiao Li, Jiazhi Ni
Venue: CIKM
Year: 2025

Rapid urban growth and road network evolution make it increasingly difficult to maintain accurate digital maps. Traditional manual or satellite-based updates are often delayed or insufficiently detailed. The trajectory-to-road (T2R) task addresses these limitations by leveraging GPS trajectory data to reconstruct up-to-date road networks, providing a scalable solution for navigation, ride-hailing, and urban planning. Existing T2R methods face significant challenges due to their reliance on numerous statistical features and limited generative capabilities. Additionally, current datasets are often outdated and come from a single mobility source, leading to biased urban dynamics and poor generalizability. To address these issues, we introduce DiffusionT2R, the first diffusion-based framework for T2R. DiffusionT2R leverages three key innovations: a multi-channel trajectory representation to provide fine-grained conditioning for guiding the denoising process; a Dual-Level Mixture of Filters that enhances feature extraction at both local and global scales; and a consistency constraint to ensure spatial alignment with input trajectories, preserving road network realism. We also present the largest available trajectory dataset with up-to-date road networks, diverse mobility patterns and high-quality filtering. Experimental results show that DiffusionT2R outperforms existing methods, delivering accurate, realistic, and generalizable road networks with improved robustness in real-world scenarios. The dataset TXBJ is available at https://github.com/ywyangwang/TXBJ.
Tide: A Time-Wise Causal Debiasing Framework for Generative Dynamic Link Prediction
Authors: Xin Zhang, Jianming Zheng, Fei Cai, Zhiqiang Pan, Wanyu Chen, Chonghao Chen, Honghui Chen
Venue: CIKM
Year: 2025

Dynamic link prediction aims to predict the future links in dynamic graphs. Existing generative dynamic link prediction studies utilize the global degree distribution for mitigating the over-estimation problem, which can model the time-invariant features while neglecting the time-varying features, resulting in capturing inaccurate evolution patterns. However, such time related features are intrinsically coupled, which makes simultaneously and independently modeling both features infeasible. Motivated by these issues, we propose a Time-wise causal debiasing framework (Tide) for generative dynamic link prediction, which does not resort to any extra trainable modules. Instead, to obtain the time-invariant features, we first utilize a time-invariant deconfounded learning mechanism for decoupling the prediction score with the degree distribution. To leverage the time-varying features, we intervene in the model during the inference stage by a predicted future degree distribution, aiming to make the accurate predictions for dynamic graphs. Experiments conducted on four public datasets under both inductive and transductive settings present that our Tide enhanced models can outperform their corresponding vanilla versions by up to 21.42% and 27.73% in terms of NDCG and Jaccard, respectively.
Explainable Numerical Claim Verification
Authors: Peter Røysland Aarnes
Venue: CIKM
Year: 2025

The rapid proliferation of mis- and disinformation in the digital age highlights the urgent need for scalable, transparent, and trustworthy automated fact-checking systems. Large Language Models (LLMs) offer strong language understanding capabilities but suffer from opacity and brittleness, particularly in reasoning over numerical claims. This work explores how Explainable Artificial Intelligence (XAI)-through the lens of counterfactual explanations and adversarial training-can be used to systematically evaluate and improve the robustness of LLMs against perturbed numerical inputs. We propose a framework that employs counterfactual generation to both probe LLM reliability and generate user-appropriate explanations. Through empirical evaluations using a large-scale numerical fact-checking dataset (QuanTemp), we show that even state-of-the-art LLMs are susceptible to subtle numerical perturbations, impacting verdict accuracy. Our methodology contributes a dual-purpose diagnostic and training strategy that not only bolsters robustness but also enables both global and local interpretability-thereby improving explainability in automated fact-checking systems.