Large Multimodal Models (LMMs) face notable challenges when encountering multimodal knowledge conflicts, particularly under retrieval-augmented generation (RAG) frameworks, where the contextual information from external sources may contradict the model’s internal parametric knowledge, leading to unreliable outputs. However, existing benchmarks fail to reflect such realistic conflict scenarios. Most focus solely on intra-memory conflicts, while context-memory and inter-context conflicts remain largely unaddressed. Furthermore, commonly used factual knowledge-based evaluations are often overlooked, and existing datasets lack a thorough investigation into conflict detection capabilities.To bridge this gap, we propose MMKC-Bench, a benchmark designed to evaluate factual knowledge conflicts in both context-memory and inter-context scenarios. MMKC-Bench encompasses four types of multimodal knowledge conflicts and includes 1,881 knowledge instances and 3,997 images across 32 broad types, collected through automated pipelines with human verification. We evaluate four representative series of LMMs on both model behavior analysis and conflict detection tasks. Our findings show that while current LMMs are capable of recognizing knowledge conflicts, they tend to favor internal parametric knowledge over external evidence. We hope MMKC-Bench will foster further research in multimodal knowledge conflict and enhance the development of multimodal RAG systems.

Modeling asynchronous event sequences is crucial in numerous real-world applications such as healthcare monitoring, financial transaction analysis, and so on. Traditional temporal point processes, including Hawkes Processes, often fail to capture complex dependencies due to their parametric limitations. While neural approaches like RNNs and Transformers have improved flexibility, they struggle with computational inefficiency, and attention saturation. In this paper, we introduce the Mamba Hawkes Process (MHP), the first framework to integrate selective state space model (Mamba) with temporal point processes. MHP leverages time-varying state transitions and input-dependent gating to efficiently encode event history and capture long-term dependencies with linear complexity. Importantly, we provide theoretical guarantees showing that MHP generalizes both classical multi-exponential Hawkes processes and exponential-decay gated RNNs, underscoring its expressive power and theoretical soundness. To address the inherent constraints of pure state space models in handling heterogeneous event interactions, we further develop Adaptive Mamba Hawkes Process (A-MHP) that incorporates two novel mechanisms: a Time-Scaling Mechanism that adaptively weights time intervals based on event type and history, and a Dual-Channel State Transition that adaptively processes event content and temporal dynamics for more refined state updates. Extensive experiments on synthetic and real-world datasets demonstrate that MHP and A-MHP consistently outperform state-of-the-art baselines in event prediction tasks, particularly in long-sequence scenarios. Our work establishes a scalable and theoretically grounded paradigm for event sequence modeling, with practical implications for predictive maintenance, anomaly detection, and dynamic system analysis. The code is available at https://github.com/Ethan-Shen-Individual-Lab/Mamba-Hawkes-Process.

The widely publicized impact of user-generated review (UGR) sites on sales has spawned a large number of studies on user behaviors on UGR sites. These existing studies center on conversions of visitors into paying customers, and the creation and dissemination of online reviews. UGR site visitors will consider emotional cues in reviews more seriously if the product offered is more enjoyment-based, hedonic, than utilitarian. This study examines the impact of user enjoyment, when reading reviews on hedonic products, on dissemination of those reviews with their personal social circles. Specifically, to examine user enjoyment on UGR sites, we apply the concept of flow – enjoyable experiences that are intense and enticing enough to convert visitors to endorsers. We conducted two online surveys to contrast our suggested flow-based explanation on dissemination against alternative rationality-based one. The findings from our surveys show that the flow state increases their intentions to recommend the establishments, while the objectivity of reviews and expertise of reviewers are limited in explaining such.

As an important category of user-generated content (UGC) community, Question and Answer (Q&A) community offers internet users opportunities to ask questions and share knowledge with others. In order to understand how the ratings of knowledge contribution quality correlate with the way knowledge is being shared in discussion threads, the study examines user behaviors and profiles in a large knowledge sharing community, /r/Techsupport, a discussion based Q&A site in Reddit.com concerning internet and technology problems. Negative binomial regressions and negative binomial mixed models are built to investigate the relationships among thread structure, level of user activity, user profiles and the ratings of threads and comments in the community. Results indicate that in the better rated threads, the structures tend to be more centralized with heterogeneous participants discussing the problem at a deeper level. Meanwhile, contributions with good ratings are more likely to be produced by users who are more engaged in commenting behaviors.