OpenRaiser/Intern-Atlas huggingface dataset:
MSTI-Plus: Introducing Non-Sarcasm Reference Materials to Enhance Multimodal Sarcasm Target Identification
Authors: Fengmao Lv, Mengting Xiong, Junlin Fang, Lingli Zhang, Tianze Luo, Weichao Liang, Tianrui Li
Venue: WWW
Year: 2025

Sarcasm is a subtle expression that indicates the incongruity between literal meanings and factual opinions. For multimodal posts in social medias which consist of both images and texts, sarcasm expressions are even more widespread. Recent works have paid attentions to Multimodal Sarcasm Target Identification (MSTI), which focuses on detecting aspect terms of mockery or ridicule as sarcasm targets. However, the current MSTI benchmark only contains annotations on fine-grained sarcasm targets within sarcastic samples. In practice, it will be featured by two major limitations. First, there lack annotations on non-sarcasm aspects to inform deep models to perceive the semantic difference between sarcasm targets and non-sarcasm aspects. As a result, deep models will tend to incorrectly recognize non-sarcasm aspects as sarcasm targets. Second, there lack non-sarcasm samples to inform deep models to perceive the inherent semantics of sarcasm intentions. Due to the subtle characteristic of sarcasm expressions, models trained with only fine-grained supervision signals cannot thoroughly understand the sarcasm semantics, making the fine-grained task of sarcasm target identification restricted. Motivated by these limitations, this work reconstructs a more comprehensive MSTI benchmark by introducing both fine-grained non-sarcasm aspect annotations for existing sarcasm samples and non-sarcastic samples as non-sarcasm references to enable deep models to clearly perceive the mentioned information during training. Based on the multi-granularity (i.e., both aspect-level and sample-level) non-sarcasm information introduced into this new benchmark, this work further proposes a pluggable Semantics-aware Sarcasm Target Identification mechanism to enhance sarcasm target identification by modeling the overall semantics of sarcasm intentions via an auxiliary sample-level sarcasm recognition task. By modeling the overall semantics of sarcasm intention, deep models can obtain a more comprehensive understanding on sarcasm semantics, leading to improved performance on fine-grained sarcasm target identification. Extensive experiments are conducted to validate our contribution. Both the dataset and code are available at https://github.com/tiggers23/MSTI-Plus.
Coreness Maximization through Budget-Limited Edge Insertion
Authors: Xiaowei Lv, Xiaojia Xu, Yongcai Wang, Haoyu Liu, Deying Li
Venue: WWW
Year: 2025

The Budget Limited Coreness Maximization (BLCM) problem aims to enhance average user engagement by activating a limited number of connections, i.e., inserting up to b edges to maximize the coreness gain of all vertices in a graph. Due to the cascading feature, we prove the BLCM is NP-hard, APX-hard, and not submodular, meaning greedy sequential edge insertion fails to deliver satisfactory results. As a result, solving BLCM requires combinatorial edge insertion and must face the combinatorial exploration difficulty. This paper proposes the first effective and polynomial-time approach to BLCM. It embeds local combinatorial optimization into global greedy search to boost the benefits of combinatorial optimization while restricting its complexity. Specifically, we propose efficient methods to evaluate the cascaded coreness improvements of two local combinatorial strategies, i.e., when a leader or a group of nodes increase their coreness values via local edge insertion. Note that the key difficulty lies in evaluating the cascading effects. Based on these, we propose three efficient combinatorial edge insertion strategies: (1) Leader-Centric Greedy Insertion (LCGI), (2) Group-Centric Greedy Insertion (GCGI), and (3) a Leader-Group Balance (LGB) insertion. LCGI greedily finds the most influential leader that can produce the highest coreness gain together with its followers. GCGI finds the most influential group that can promote the most coreness gain. LGB combines the two strategies to select edge combinations adaptively. We prove the low complexity of LCGI, GCGI and LGB. Experiments conducted on 13 real-world datasets highlight their practical utility and superiority over existing approaches.
A Multiagent-based Framework on RoboCup Simulation System for Enhancing Rescue Operation during Dynamic Disaster Environments
Authors: Qihua Lyu, Xin Cao, Yongsheng Yu, Shan Xue, Jian Yang, Jia Wu, Amin Beheshti
Venue: WWW
Year: 2025

Disasters, such as fires, earthquakes, and hurricanes, inflict significant losses on human lives and economies. These unfortunate events have driven research into best-practice rescue methods via the RoboCupRescue competition and simulation system. One crucial aspect of rescue management is to ensure police clear ingresses and egresses to allow quick evacuations for citizens and efficient movement for other rescue personnel. To this end, existing rescue methods mainly rely on the A* path-planning method coupled with a nearest-neighbour clearance strategy. However, the A* path-planning method leads to routes selected for searching blockages that ignore the prevailing disaster conditions. The nearest-neighbour clearance strategy often results in curved clearance that takes more than the optimal times. Hence, to enhance the efficiency of police rescue, we propose a novel framework called adaptive A* graph routing with smoothed obstacle clearance. This framework incorporates an adaptive A* graph routing approach that will select paths based on the particular disaster conditions. Additionally, it uses a smoothed obstacle clearance strategy such that police will clear blocked roads in a particular ''smoothed'' direction. This framework optimizes route selection and obstacle clearance for police, improving rescue efficiency. Experimental results demonstrate that our proposed framework achieves superior rescue outcomes compared to the current baselines.
Wikipedia Contributions in the Wake of ChatGPT
Authors: Liang Lyu, James Siderius, Hannah Li, Daron Acemoglu, Daniel P. Huttenlocher, Asuman E. Ozdaglar
Venue: WWW
Year: 2025

How has Wikipedia activity changed for articles with content similar to ChatGPT following its introduction? We estimate the impact using differences-in-differences models, with dissimilar Wikipedia articles as a baseline for comparison, to examine how changes in voluntary knowledge contributions and information-seeking behavior differ by article content. Our analysis reveals that newly created, popular articles whose content overlaps with ChatGPT 3.5 saw a greater decline in editing and viewership after the November 2022 launch of ChatGPT than dissimilar articles did. These findings indicate heterogeneous substitution effects, where users selectively engage less with existing platforms when AI provides comparable content. This points to potential uneven impacts on the future of human-driven online knowledge contributions.
InCo: Exploring Inter-Trip Cooperation for Efficient Last-mile Delivery
Authors: Wenjun Lyu, Shuxin Zhong, Guang Yang, Haotian Wang, Yi Ding, Shuai Wang, Yunhuai Liu, Tian He, Desheng Zhang
Venue: WWW
Year: 2025

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.
LoCal: Logical and Causal Fact-Checking with LLM-Based Multi-Agents
Authors: Jiatong Ma, Linmei Hu, Rang Li, Wenbo Fu
Venue: WWW
Year: 2025

With the development of social media, people are exposed to a vast amount of unverified information, making fact-checking particularly important. Existing fact-checking methods primarily encourage breaking down claims into more easily solvable sub-tasks, and deriving final answers through reasoning with external evidence. However, these models face logical issues regarding whether and how the sub-tasks can logically be combined to form the original claims, and encounter causal errors in the reasoning process due to insufficient evidence or hallucinations from LLMs. In addition, they often suffer from a lack of interpretability. In this paper, we propose Logical and Causal fact-checking (LoCal), a novel fact-checking framework based on multiple LLM-based agents. The usage of multi-agent systems is due to their increasingly demonstrated ability to perform complex tasks in a manner similar to humans. LoCal primarily consists of a decomposing agent, multiple reasoning agents, and two evaluating agents. Specifically, the decomposing agent first utilizes the in-context learning ability of LLMs to break down complex claims into simpler sub-tasks, including fact verification tasks and question answering tasks. Afterwards, two types of reasoning agents are respectively utilized to retrieve external knowledge to address the fact verification tasks that require comparative analysis skills, and the question answering tasks that necessitate the ability of information extraction from evidence. We then combine the sub-tasks and their corresponding responses to generate a solution for evaluation. In order to enhance logical and causal consistency, two evaluating agents are respectively employed to examine whether the generated solution is logically equivalent to the original claim and determine whether the solution still holds when challenged by the counterfactual label. The evaluating agents provide confidence degrees for the solutions based on the evaluation results and iteratively correct the logical and causal errors in the reasoning process. We evaluate LoCal on two challenging datasets, and the results show that LoCal significantly outperforms all the baseline models across different settings of evidence availability. In addition, LoCal offers better interpretability by providing a structured solution along with detailed evaluating processes. We believe LoCal will provide valuable insights for future misinformation detection.
A Context-Aware Framework for Integrating Ad Auctions and Recommendations
Authors: Yuchao Ma, Weian Li, Yuejia Dou, Zhiyuan Su, Changyuan Yu, Qi Qi
Venue: WWW
Year: 2025

Recently, many e-commerce platforms have favored presenting a mixed list of ads and organic content to users. The widely-used approach separately ranks ads and organic items, then sequentially inserts ads into the list of organic items. However, this method yields sub-optimal results. Firstly, it only ensures that each generated ad and organic item list achieves local optimality, while the predetermined insertion order fails to guarantee global optimality. Secondly, this approach overlooks the mutual effect between organic items and ads, resulting in an incomplete utilization of contextual information. Besides, it cannot prevent strategic behavior by advertisers. Therefore, we propose a context-aware integrated framework to address these issues. This framework applies automated mechanism design to integrated ad auctions for the first time. Specifically, it models ads and organic items simultaneously along with their contextual information and employs a learning-based approach to prevent advertisers from engaging in strategic behavior. Afterward, the framework directly generates a mixed list, enhancing the overall performance. We also propose Transformer encoder-based Integrated Contextual Net work (TICNet) to generate the optimal integrated contextual ad auction. Finally, we validate the effectiveness of TICNet on synthetic and real-world datasets. Our experimental results demonstrate that TICNet significantly outperforms baseline models across multiple metrics.
Seek Inner: LLM-Enhanced Information Mining for Medical Visual Question Answering
Authors: Ao Ma, Zhiyuan Li, Zhuonan Liang, Tiancheng Gu, Jianan Fan, Jieting Long, Henning Müller, Weidong Cai
Venue: WWW
Year: 2025

Medical visual question answering (Med-VQA) focuses on analyzing medical images to accurately respond to clinicians' specific questions. Although integrating prior knowledge can enhance VQA reasoning, current methods often struggle to extract relevant information from the vast and complex medical knowledge base, thereby limiting the models' ability to learn domain-specific features. To overcome this limitation, our study presents a novel information mining approach that leverages large language models (LLMs) to efficiently retrieve pertinent data. Specifically, we design a latent knowledge generation module that employs LLMs to separately extract and filter information from questions and answers, enhancing the model's inference capabilities. Furthermore, we propose a multi-level prompt fusion module in which an initial prompt interacts with the extracted latent knowledge to draw clinically relevant details from both unimodal and multimodal features. Experimental results demonstrate that our approach outperforms current state-of-the-art models on multiple Med-VQA benchmark datasets.
MSDZip: Universal Lossless Compression for Multi-source Data via Stepwise-parallel and Learning-based Prediction
Authors: Huidong Ma, Hui Sun, Liping Yi, Yanfeng Ding, Xiaoguang Liu, Gang Wang
Venue: WWW
Year: 2025

With the rapid development of the Internet, the huge amount of Multi-Source Data (MSD) brings challenges in data sharing and storing. Lossless data compression is the major way to solve those problems. Nowadays, neural-network technologies bring significant advantage in data modeling, making learning-based lossless compressors (LLCs) for multi-source data have emerged continuously. Compared with traditional compressors, the LLCs are more useful to catch complex redundancy patterns in MSD, and thus have great potential in enhancing compression ratio. However, existing LLCs still suffer from unsatisfactory compression ratios and lower throughput. To solve those problems, we propose a novel universal MSD lossless compressor called MSDZip via Stepwise-parallel and learning-based prediction technologies, it introduces two major designs: 1) We propose a Local-Global-Deep Mixing block in the learning-based prediction module to establish dependencies for MSD symbols, where designed Deep Mixing block solves the problem of unstable weights in the perceptual layers caused by cold-start problem to enhance the compression ratio significantly. 2) We design a Stepwise-parallel multi-GPU-accelerated compression strategy to address the compression speed and graphics memory constraints of single GPU in the face of large-scale data. The Stepwise-parallel module passes the source MSD to learning-based prediction model through the data chunking strategy, where the model of the previous chunk is used to guide the compression of the next chunk in parallel. We compare MSDZip with 5 classical learning-based and 6 traditional compressors on 12 well-studied real-world datasets. The experimental results demonstrate that MSDZip optimizes 3.418%-69.874% in terms of compression ratio and 31.171%-495.649% in terms of throughput compared to advanced LLCs. The source code of MSDZip and the linkages of the experimental datasets are available at https://github.com/mhuidong/MSDZip.
You Can't Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense
Authors: Wuyuao Mai, Geng Hong, Pei Chen, Xudong Pan, Baojun Liu, Yuan Zhang, Haixin Duan, Min Yang
Venue: WWW
Year: 2025

With the rise of generative large language models (LLMs) like LLaMA and ChatGPT, these models have significantly transformed daily life and work by providing advanced insights. However, as jailbreak attacks continue to circumvent built-in safety mechanisms, exploiting carefully crafted scenarios or tokens, the safety risks of LLMs have come into focus. While numerous defense strategies-such as prompt detection, modification, and model fine-tuning-have been proposed to counter these attacks, a critical question arises: do these defenses compromise the utility and usability of LLMs for legitimate users? Existing research predominantly focuses on the effectiveness of defense strategies without thoroughly examining their impact on performance, leaving a gap in understanding the trade-offs between LLM safety and performance. Our research addresses this gap by conducting a comprehensive study on the utility degradation, safety elevation, and exaggerated-safety escalation of LLMs with jailbreak defense strategies. We propose USEBench, a novel benchmark designed to evaluate these aspects, along with USEIndex, a comprehensive metric for assessing overall model performance. Through experiments on seven state-of-the-art LLMs, we found that mainstream jailbreak defenses fail to ensure both safety and performance simultaneously. Although model-finetuning performs the best overall, their effectiveness varies across LLMs. Furthermore, vertical comparisons reveal that developers commonly prioritize performance over safety when iterating or fine-tuning their LLMs.
Learning by Comparing: Boosting Multimodal Affective Computing through Ordinal Learning
Authors: Sijie Mai, Ying Zeng, Haifeng Hu
Venue: WWW
Year: 2025

Previous studies on multimodal affective computing primarily focus on approximating predictions to annotated labels, often neglecting the ordinal nature of affective states. In this paper, we address this issue by exploring ordinal learning, and a Multimodal Ordinal Affective Computing (MOAC) framework is designed to enhance the understanding of the nature of affective concepts. Specifically, we propose coarse-grained label-level ordinal learning that prompts the model to learn to compare in the label space, encouraging higher predictive values for samples annotated with larger labels over those with smaller labels. Moreover, a regularization loss is proposed to prevent the output distributions from deviating significantly from the annotated label distributions. Fine-grained feature-level ordinal learning is then performed via the feature difference operation and the neutral embedding. The former compares samples in the feature space, calculating the difference between features of different samples to generate 'new' features for a more robust training. The latter seeks to reduce the difficulty of prediction by estimating the difference between the target multimodal representations and a neutral reference. We first demonstrate MOAC in multimodal sentiment analysis, which is a regression task that aligns well with the function of ordinal learning. Then we extend MOAC to classification tasks including multimodal humor detection and sarcasm detection to evaluate its generalizability. Experiments suggest that MOAC outperforms state-of-the-art methods.
A Multi-Agent Framework for Multi-Source Manufacturing Knowledge Integration and Question Answering
Authors: Teng Mao, Shuangtao Yang, Bo Fu
Venue: WWW
Year: 2025

The integration of next-generation information technologies in the manufacturing sector has resulted in the generation of extensive and complex knowledge data by organizations. Effectively managing and utilizing this dispersed knowledge presents a significant challenge. This paper proposes a Multi-Agent-based, Multi-Source, and Multimodal Retrieval-Augmented Generation (RAG) framework specifically designed for the manufacturing industry. By employing advanced methodologies such as multimodal document parsing, multi-source data integration, and intelligent agent-driven querying, the proposed framework aims to address the interconnected challenges of knowledge management and intelligent question answering in complex manufacturing environments. The principal contributions of this research include: (1) the advancement of sophisticated multimodal document parsing techniques, (2) the establishment of robust strategies for multi-source data integration, (3) the development of a comprehensive multi-agent intelligent question-answering system, and (4) the enhancement of the Thinking RAG model. This framework provides a holistic solution to the complexities associated with knowledge management and improves retrieval efficiency in the manufacturing sector.
Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
Authors: Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, Md. Shad Akhtar
Venue: WWW
Year: 2025

The expression of mental health symptoms through non-traditional means, such as memes, has gained remarkable attention over the past few years, with users often highlighting their mental health struggles through figurative intricacies within memes. While humans rely on commonsense knowledge to interpret these complex expressions, current Multimodal Language Models (MLMs) struggle to capture these figurative aspects inherent in memes. To address this gap, we introduce a novel dataset, AxiOM, derived from the GAD anxiety questionnaire, which categorizes memes into six fine-grained anxiety symptoms. Next, we propose a commonsense and domain-enriched framework, M3H, to enhance MLMs' ability to interpret figurative language and commonsense knowledge. The overarching goal remains to first understand and then classify the mental health symptoms expressed in memes. We benchmark M3H against 6 competitive baselines (with 20 variations), demonstrating improvements in both quantitative and qualitative metrics, including a detailed human evaluation. We observe a clear improvement of 4.20% and 4.66% on weighted-F1 metric. To assess the generalizability, we perform extensive experiments on a public dataset, RESTORE, for depressive symptom identification, presenting an ablation study that highlights the contribution of each module. Our findings reveal limitations in existing models and the advantage of employing commonsense to enhance figurative understanding.