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Xiaofan Zheng, Zinan Zeng 0001, Heng Wang 0008, Yuyang Bai, Yuhan Liu 0028, Minnan Luo

The rapid development of social media has led to a surge of eye-catching fake news on the Internet, with multimodal news comprising both images and text being particularly prevalent. To address the challenges of Multimodal Fake News Detection (MFND), numerous supervised task-specific Multimodal Small Language Models (MSLMs) have been developed. However, these models lack the breadth of knowledge and the depth of language understanding, which results in unsatisfactory adaptability, generalization, and explainability performance. To address these issues, we attempt to introduce Large Vision-Language Models (LVLMs), aiming to leverage the common sense understanding and logical reasoning abilities of LVLMs for the MFND task. We observed that LVLMs can generate reasonable analyses of news content from specific angles. However, when it comes to synthesizing these analyses for final judgment, their performance declines significantly, failing to meet the accuracy benchmarks set by existing MSLMs detection models. This reflects the need for a more effective way for LVLMs, which have not undergone task-specific training, to utilize their knowledge and capabilities. Based on these findings, we propose the Explainable Adaptive Rationale-Augmented Multimodal (EARAM) framework, which adaptively uses MSLMs to extract useful rationales from the multi-perspective analyses of LVLMs. After making judgments based on these rationales, EARAM then assists LVLMs in generating more reliable explanations. Extensive experiments demonstrate that our model not only achieves state-of-the-art results on widely used datasets but also significantly outperforms other models in terms of generalization and explainability.

Yuheng Zhang, Yuan Yuan 0032, Jingtao Ding, Jian Yuan, Yong Li 0008

With global urbanization, the focus on sustainable cities has largely grown, driving research into equity, resilience, and urban planning, which often relies on mobility data. The rise of web-based apps and mobile devices has provided valuable user data for mobility-related research. However, real-world mobility data is costly and raises privacy concerns. To protect privacy while retaining key features of real-world movement, the demand for synthetic data has steadily increased. Recent advances in diffusion models have shown great potential for mobility trajectory generation due to their ability to model randomness and uncertainty. However, existing approaches often directly apply identically distributed (i.i.d.) noise sampling from image generation techniques, which fail to account for the spatiotemporal correlations and social interactions that shape urban mobility patterns. In this paper, we propose CoDiffMob, a diffusion model for urban mobility generation with collaborative noise priors, we emphasize the critical role of noise in diffusion models for generating mobility data. By leveraging both individual movement characteristics and population-wide dynamics, we construct novel collaborative noise priors that provide richer and more informative guidance throughout the generation process. Extensive experiments demonstrate the superiority of our method, with generated data accurately capturing both individual preferences and collective patterns, achieving an improvement of over 32%. Furthermore, it can effectively replace web-derived mobility data to better support downstream applications, while safeguarding user privacy and fostering a more secure and ethical web. This highlights its tremendous potential for applications in sustainable city-related research. The code and data are available at https://github.com/tsinghua-fib-lab/CoDiffMob.

Yunke Zhang, Ruolong Ma, Xin Zhang 0106, Yong Li 0008

The rapid pace of urbanization has led to unequal benefits for residents, creating significant inequality issues and discussions around Sustainable Development Goals 10 and 11. Accurate measurement of inequality within urban areas is essential for effective mitigation strategies. Traditional methods rely on survey-based census data, which are time-consuming and delayed, while some studies use coarse proxies like nighttime lights. However, these methods are limited by resolution and fail to capture fine-grained disparities within communities. To address this, we aim to leverage accessible urban imagery, which offers detailed visual features. Two key challenges must be addressed: 1) accurately perceiving micro-level inequalities within neighborhoods, and 2) ensuring that this perception is interpretable for policy guidance. To address these gaps, we propose UI-CoT, a framework that leverages the power of urban imagery-based visual language models in urban inequality perceiving, enhanced by Chain-of-Thought prompting to improve reasoning capabilities. We fine-tune a visual language model to predict three essential neighborhood inequality indicators: the income Gini coefficient, dominant race, and racial income ratio. Extensive experiments show that our model can effectively perceive micro-level inequalities, with the incorporation of Chain-of-Thought reasoning further improving the model's performance by 17.2%. This research offers valuable insights into addressing inequalities within urban environments and demonstrates the potential of web resources in empowering urban sustainable development. The code and data are available at https://github.com/tsinghua-fib-lab/UI-CoT.

Ziheng Zhang, Zhenxi Lin, Yefeng Zheng 0001, Xian Wu 0001

Previous evaluation frameworks for large language models (LLMs) have mostly relied on existing question-answering benchmarks, which are primarily task-oriented rather than knowledge-oriented. In the medical domain, however, the effective deployment of LLMs necessitates a thorough evaluation of their medical knowledge coverage. To this end, we propose a systematic evaluation framework, MedKGEval, to assess the coverage of medical knowledge in LLMs through the lens of medical knowledge graphs (KGs). MedKGEval transforms various levels of knowledge (entity-level, relation-level, and subgraph-level) from the medical KG into distinct groups of question-answer pairs, which serve as comprehensive evaluation benchmarks. In addition to traditional task-oriented evaluations, MedKGEval introduces a novel knowledge-oriented evaluation approach that encompasses the assessment of knowledge coverage across entities, relations, and triples. This multi-aspect evaluation approach allows for a more nuanced understanding of LLMs' knowledge coverage in the medical context. Using these benchmarks, we conduct a systematic evaluation of 11 LLMs from multiple perspectives, revealing insights into their strengths and weaknesses in medical knowledge memorization and reasoning.

Huayi Yang, Chunyuan Zheng 0001, Guorui Liao, Shanshan Huang 0004, Jun Liao 0001, Zhili Gong 0001, Haoxuan Li 0001, Li Liu 0001

A significant challenge in air quality index (AQI) prediction is to accurately evaluate the potential outcomes after conducting interventions in pollutant factors such as industrial emissions for each enterprise. Existed methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. Specifically, we employ the front-door adjustment to explicitly eliminate unmeasured confounders by intervening in industrial emissions from the target enterprise. Meanwhile, we take industrial emissions of neighboring enterprises into account when intervening in the target enterprise and simulate the dispersion of industrial emissions through a Gaussian plume model based on meteorological factors. Experiments on two real-world datasets validate the superior performance of our model on AQI prediction compared to the state-of-the-art baselines.

Jie Yang 0088, Rui Zhang 0003, Ziyang Cheng, Dawei Cheng, Guang Yang 0057, Bo Wang 0162

Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more sophisticated camouflage strategies. Specifically, fraudsters disguise themselves by mimicking the behavioral data collected by platforms, ensuring that their key characteristics are consistent with those of benign users to a high degree, which we call Adaptive Camouflage. Consequently, this narrows the differences in behavioral traits between them and benign users within the platform's database, thereby making current GFD models lose efficiency. To address this problem, we propose a relation diffusion-based graph augmentation model Grad. In detail, Grad leverages a supervised graph contrastive learning module to enhance the fraud-benign difference and employs a guided relation diffusion generator to generate auxiliary homophilic relations from scratch. Based on these, weak fraudulent signals would be enhanced during the aggregation process, thus being obvious enough to be captured. Extensive experiments have been conducted on two real-world datasets provided by WeChat Pay, one of the largest online payment platforms with billions of users, and three public datasets. The results show that our proposed model Grad outperforms SOTA methods in both various scenarios, achieving at most 11.10% and 43.95% increases in AUC and AP, respectively.

Xuankai Yang 0001, Yan Wang 0002, Xiuzhen Zhang 0001, Shoujin Wang, Huaxiong Wang, Kwok-Yan Lam

Cross-domain fake news detection aims to mitigate domain shift and improve detection performance by transferring knowledge across domains. Existing approaches transfer knowledge based on news content and user engagements from a source domain to a target domain. However, these approaches face two main limitations, hindering effective knowledge transfer and optimal fake news detection performance. Firstly, from a micro perspective, they neglect the negative impact of veracity-irrelevant features in news content when transferring domain-shared features across domains. Secondly, from a macro perspective, existing approaches ignore the relationship between user engagement and news content, which reveals shared behaviors of common users across domains and can facilitate more effective knowledge transfer. To address these limitations, we propose a novel macro- and micro- hierarchical transfer learning framework (MMHT) for cross-domain fake news detection. Firstly, we propose a micro-hierarchical disentangling module to disentangle veracity-relevant and veracity-irrelevant features from news content in the source domain for improving fake news detection performance in the target domain. Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms the state-of-the-art baselines.

Qingzheng Xu, Heming Du, Szymon Lukasik, Tianqing Zhu, Sen Wang 0001, Xin Yu 0002

Misinformation is a significant societal issue with potentially severe consequences. It appears in text, image, audio, and video modalities, encompassing various categories such as unimodal deception (fact-conflicting, AI-generated & offensive content) and cross-modal inconsistencies. However, current detection approaches often focus on text and image, overlooking the growing prevalence of misinformation in audio and video content. Moreover, these methods typically tend to address only one or two types of misinformation, failing to address all categories simultaneously. These detectors are also usually designed to make judgments without providing explanations, reducing transparency and limiting their broader applicability. To address these issues, we propose MDAM3, a Misinformation Detection and Analysis Framework for Multitype Multimodal Media. MDAM3 analyzes each input in internal detection and examines relationships across modalities to identify inconsistencies. It utilizes web resources and integrates Large Vision-Language Models (LVLMs) to deliver accurate detection results along with detailed analysis. To evaluate MDAM3, we curate MDAM3-DB, a specialized multitype multimodal misinformation dataset. A user study is conducted to explore MDAM3's usability, interpretability, and effectiveness. We hope this research contributes to advancing misinformation detection methodologies and provides valuable insights for developing robust multimodal analysis tools.

Sheng Xiang 0001, Yidong Jiang, Yunting Chen, Dawei Cheng, Guoping Zhao, Changjun Jiang 0002

Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overlooking the broader context of interconnected nodes. Graph-based techniques, particularly Graph Neural Networks (GNNs), have advanced the field by leveraging relational information effectively. However, in real-world spoofing detection datasets, trading behaviors exhibit dynamic, irregular patterns. Existing spoofing detection methods, though effective in some scenarios, struggle to capture the complexity of dynamic and diverse, evolving inter-node relationships. To address these challenges, we propose a novel framework called the Generative Dynamic Graph Model (GDGM), which models dynamic trading behaviors and the relationships among nodes to learn representations for conspiracy spoofing detection. Specifically, our approach incorporates the generative dynamic latent space to capture the temporal patterns and evolving market conditions. Raw trading data is first converted into time-stamped sequences. Then we model trading behaviors using the neural ordinary differential equations and gated recurrent units, to generate the representation incorporating temporal dynamics of spoofing patterns. Furthermore, pseudo-label generation and heterogeneous aggregation techniques are employed to gather relevant information and enhance the detection performance for conspiratorial spoofing behaviors. Experiments conducted on spoofing detection datasets demonstrate that our approach outperforms state-of-the-art models in detection accuracy. Additionally, our spoofing detection system has been successfully deployed in one of the largest global trading markets, further validating the practical applicability and performance of the proposed method.

Wei Wu 0011, Shiqi Li, Ling Chen 0006, Fangfang Li 0004, Chuan Luo 0002

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. We have released the source code and the datasets in https://github.com/AIandBD/graph-hashing/tree/main/VLS2ketch. .

Han Wang 0053, Rui Yang Tan, Roy Ka-Wei Lee

Detecting hate speech in online content is essential to ensuring safer digital spaces. While significant progress has been made in text and meme modalities, video-based hate speech detection remains under-explored, hindered by a lack of annotated datasets and the high cost of video annotation. This gap is particularly problematic given the growing reliance on large models, which demand substantial amounts of training data. To address this challenge, we leverage meme datasets as both a substitution and an augmentation strategy for training hateful video detection models. Our approach introduces a human-assisted reannotation pipeline to align meme dataset labels with video datasets, ensuring consistency with minimal labeling effort. Using two state-of-the-art vision-language models, we demonstrate that meme data can substitute for video data in resource-scarce scenarios and augment video datasets to achieve further performance gains. Our results consistently outperform state-of-the-art benchmarks, showcasing the potential of cross-modal transfer learning for advancing hateful video detection. Dataset and code are available at (https://github.com/Social-AI-Studio/CrossModalTransferLearning).

Lin Tian, Emily Booth, Francesco Bailo, Julian Droogan, Marian-Andrei Rizoiu

In today's digital age, conspiracies and information campaigns can emerge rapidly and erode social and democratic cohesion. While recent deep learning approaches have made progress in modeling engagement through language and propagation models, they struggle with irregularly sampled data and early trajectory assessment. We present IC-Mamba, a novel state space model that forecasts social media engagement by modeling interval-censored data with integrated temporal embeddings. Our model excels at predicting engagement patterns within the crucial first 15-30 minutes of posting (RMSE 0.118-0.143), enabling rapid assessment of content reach. By incorporating interval-censored modeling into the state space framework, IC-Mamba captures fine-grained temporal dynamics of engagement growth, achieving a 4.72% improvement over state-of-the-art across multiple engagement metrics (likes, shares, comments, and emojis). Our experiments demonstrate IC-Mamba's effectiveness in forecasting both post-level dynamics and broader narrative patterns (F1 0.508-0.751 for narrative-level predictions). The model maintains strong predictive performance across extended time horizons, successfully forecasting opinion-level engagement up to 28 days ahead using observation windows of 3-10 days. These capabilities enable earlier identification of potentially problematic content, providing crucial lead time for designing and implementing countermeasures. Code is available at: https://github.com/ltian678/ic-mamba. An interactive dashboard demonstrating our results is available at: https://ic-mamba.behavioral-ds.science/.

Jiehao Tang, Wenjun Wang, Dawei Cheng, Hui Zhao, Changjun Jiang 0002

In the global financial market, assessing bank credit ratings is essential for evaluating financial health, managing risk, and safeguarding systemic stability. While risk can transmit rapidly within the interbank lending network, timely incorporation of the latest financial disclosures to update bank ratings is vital in the swiftly evolving financial markets. However, existing approaches primarily conduct credit rating tasks using end-to-end models trained on historical financial data, thereby overlooking the staggered timing of financial disclosure from banks. Limited excavation of the credit rating records and the temporal distribution shifts existed in different financial periods still pose challenges to improving the accuracy of the credit rating tasks. To address these challenges, in this work we propose a Dual Pairwise pre-training and prompt Tuning framework with Aligned Prototypes (DPTAP) for interbank credit rating, which enables dynamic credit updates. Specifically, the dual pairwise pre-training strategy allows the framework to capture direction and distance discrepancies between rating categories. To alleviate the adverse impact of temporal distribution shifts in quarters, the latest financial features are prompted to dynamically map the patterns of the corresponding banks in the last quarter. Furthermore, we integrate rating guides from two consecutive quarters into a set of aligned prototypes to enhance supervision during the prompt tuning process. We conducted extensive experiments on a real-world bank dataset globally in the latest 8 years. The results demonstrate the superiority of our proposed framework over various competitive models, highlighting its notable capabilities in early warning and risk contagion forecasting.

Jinglin Sun, Basem Suleiman, Imdad Ullah, Imran Razzak

Preserving individual privacy is crucial when interacting with Large Language Models (LLMs) during both training and inference stages. Privacy leakage at either stage can lead to irreversible negative consequences. Although data-level privacy-preserving algorithms have been developed for smaller Natural Language Processing (NLP) models, their application to LLMs has not been extensively explored. Moreover, with plenty of algorithms emerging, it brings challenges for organizations or researchers to compare and evaluate these different algorithms to select the most suitable one for their certain requirements. To address these challenges, we introduce ''Privacy-preserving4LLM Benchmarking'', a systematic evaluation framework that systematically assesses different privacy-preserving algorithms' utility-privacy trade-offs across different LLM architectures. Our framework evaluates these algorithms in three practical scenarios: protecting training data only, user queries only, and both. We also introduce a novel Parameter Optimizer to ensure fair comparisons. To quantify privacy protection levels, we use exposure metrics, where canary data sequences are intentionally inserted into training data to measure information memorization and potential leakage. Our study presents a comprehensive empirical analysis comparing three privacy-preserving algorithms across three LLM architectures (Mistral-7B, Llama2-7b, Falcon-7b) using three different datasets. Our findings reveal that algorithm selection, protection scenarios, LLM architectures, and privacy budget settings all impact the utility and privacy level.

Xin Shen, Heming Du, Hongwei Sheng, Lincheng Li, Kaihao Zhang

Effective communication between the deaf community and hearing individuals facilitates social inclusion, equal opportunities, and the dignity of vulnerable populations. However, existing region-specific sign language systems are constrained by limited training datasets and narrow topic domains, rendering them ineffective for bridging the linguistic gaps between sign languages and spoken languages. Auslan, as the sign language specific to Australia, still lacks a reliable bidirectional translation tool for effective communication. To address these challenges, we propose AuslanWeb, a web-based system for bidirectional translation of both isolated and successive sign language. For the former, AuslanWeb achieves high-precision mapping between isolated signs (glosses) and spoken language words or phrases through a multimodal recognition system and a versatile Auslan dictionary. For the latter, it leverages the advanced contextual understanding and text generation capabilities of Large Language Models (LLMs) to support bidirectional translation between successive sign language videos and long-form spoken language. By integrating linguistic structure with advanced AI capabilities, AuslanWeb overcomes the limitations of dataset dependency and enhances the scalability of sign language translation systems. The effectiveness of the system is further validated through user feedback, receiving consistent praise from Auslan experts, Australian deaf individuals, and volunteers. The demo video of AuslanWeb is provided here.

Jinghua Piao, Zhihong Lu, Chen Gao 0001, Yong Li 0008

Recent advances in the large language models (LLM) have empowered traditional bots to gain human-level intelligence and exhibit human-like social behaviors, giving rise to a new form of LLM-driven social agents. However, the inherent limitations in LLMs could potentially result in politically biased behaviors of these agents, posing unexpected risks to human society. While great efforts have been made to examine political bias and related concerns in traditional bots and LLMs, little is known about the existence, unique characteristics, underlying origins, and potential mitigation strategies of this bias in LLM-driven social agents. To address this gap, we systematically assess political bias in LLM-driven social agents, by examining how it emerges as these agents self-reflect, communicate, and understand others during social interactions. Through designing and implementing social experiments, we discover that this bias consistently manifests in the social behaviors of agents driven by diverse LLMs, across nine key political topics. Inspired by the social learning theory, we propose to mitigate political bias by guiding these agents to emulate how humans learn to behave. By incorporating self-regulated and role-model learning processes, we reduce their political bias by 4.89% to 51.26% across diverse LLMs and topics, demonstrating the effectiveness and generalizability of the proposed strategy. This study not only advances the understanding of political bias in emerging LLM-driven agents, but also offers insights into harnessing social bots for social good.

Usman Naseem, Liang Hu 0008, Qi Zhang 0020, Shoujin Wang, Shoaib Jameel

User-generated content on social media platforms provides a valuable resource for developing automated computational methods to detect mental health issues online leading to suicidal thoughts automatically. Although current fully automated methods show promise, they may produce uncertain predictions, leading to flawed conclusions. To address this, we propose a novel model called DiGrI, or Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection, which reformulates suicide ideation assessment as a selective, prioritized prediction problem. The model incorporates a novel multi-classifier distorted greedy model that is optimized to operate under various levels of automation and abstains from making uncertain predictions with theoretical guarantees. Our results show that DiGrI outperforms strong comparative models including large language models in detecting mental health issues on a publicly available Reddit dataset. We discuss the empirical and practical implications, including the ethical considerations of using DiGrI for online automatic suicide ideation detection involving humans, if it were to be translated for use in clinical and public health practice.

Wenjun Lyu, Shuxin Zhong, Guang Yang 0028, Haotian Wang 0008, Yi Ding 0011, Shuai Wang 0008, Yunhuai Liu, Tian He 0001, Desheng Zhang 0002

An efficient last-mile delivery scheme in logistics benefits customers, couriers, and the platform. In practice, the delivery scope of a delivery station is divided into multiple areas, each of which is covered by a courier. The long distances between the delivery station and areas limit the couriers' delivery efficiency given that they need to travel back and forth multiple times a day. To solve this problem, we explore an inter-trip cooperation scheme for last-mile delivery, in which couriers traveling to the delivery station and back to corresponding areas earlier can help to take others' orders back. Coordinating the courier cooperation is challenging because we need to consider the courier's status, e.g., locations, and vehicle capacity constraint simultaneously. In this work, we design an inter-trip cooperation-based last-mile delivery system, InCo, aiming to minimize the average order delivery time. InCo includes two components: i) a time-aware spanning tree algorithm to generate the cooperation result for a group of couriers; and ii) a capacity-constrained courier grouping algorithm to optimize the courier grouping result iteratively. Extensive evaluation results with real-world order data collected from one of the largest logistics companies show that InCo improves the average saved delivery time and reduces average travel time by up to 80.2% and 28.4%, respectively, compared to baseline methods. The deployment results show InCo improves the average courier working efficiency by 21.6% to the state-of-the-practice.

Ting Long, Li'ang Yin, Yi Chang 0001, Wei Xia 0001, Yong Yu 0001

A question-answering (QA) simulator is a model that simulates human students QA behaviors. By leveraging QA history to estimate the probability of correctly answering a newly recommended question, the simulator enables the educational recommender systems to be trained in a simulated environment, protecting human students from the potential negative impact of low-quality recommendations. Despite its significant importance, the construction of QA simulators has not been thoroughly explored in the research domain of AI. Previous methods mainly rely on existing knowledge tracing (KT) models to construct such a simulator. However, due to the discrepancy between the KT task and the simulation task, those KT-based simulators suffer from severe bias accumulation, which limits the effectiveness of the simulation. In this paper, we propose a method called Diffusion-based Simulator (DSim), which takes advantage of diffusion to alleviate the bias accumulation. To our knowledge, DSim is the first to focus on building a QA simulator.

Yishuo Li, Qi Zhang 0020, Wenpeng Lu, Xueping Peng, Weiyu Zhang 0001, Jiasheng Si, Yongshun Gong, Liang Hu 0004

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients' Dynamic Treatment Regimes (DTRs) determine how patients' drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients' successive visits, which is critical to indicate patients' treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient's guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. Our code is available at: https://github.com/liyifo/MR-DTR.