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Abstract Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on pre-training datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning, masked image modeling and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions. 

Discovering urban functional zones (UFZs) is critical for understanding city spatial structures and supporting effective urban planning. Existing approaches to UFZ discovery typically rely on one of three costly strategies: (1) training large vision models directly on satellite imagery, which demands substantial computational resources; (2) leveraging crowdsourced data such as Points of Interest (POIs) from platforms like OpenStreetMap, which may be incomplete, inconsistent, or unavailable in many regions; or (3) collecting custom labeled data, which requires significant time, expense, and expert effort. Recently, large multi-modal models (LMMs) emerged as a promising alternative, offering strong capabilities in interpreting visual content without requiring extensive data labeling. However, their performance remains limited when applied to the UFZ discovery task, often struggling to capture the complex spatial and functional details and interactions of urban regions. To address this challenge, we propose a new approach that enhances LMMs’ reasoning capability to recognize urban functional zones by keeping LMM encoders frozen while training only lightweight graph-based models, eliminating the need for LMM fine-tuning or additional pre-training. Specifically, our approach first partitions the target area into small regions by road network, where an LMM is used for each region to generate visual and textual embeddings independently using its image and text encoders. Then, two graphs are constructed in which nodes represent regions with features defined by their respective embeddings, and edges encode their spatial adjacency. Message-passing on the two graphs hence captures spatial correlation between the visual and textual modalities. After that, graph clustering will suggest the prototypes representing nearby zones with similar urban functions, where contrastive learning is further leveraged to encourage cross-modal consistency. Evaluation on four city districts, namely Philadelphia (PA, USA), Pudong (Shanghai, China), San Francisco (CA, USA), and Seattle (WA, USA), substantiates the effectiveness of our proposal and that its performance is on par with supervised competitors. 

Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise in identifying these structures in the form of a directed acyclic graph (DAG), existing methods often lack efficiency, making them unsuitable for online applications. In this paper, we propose MARLIN, an efficient multi-agent RL-based approach for incremental DAG learning. MARLIN uses a DAG generation policy that maps a continuous real-valued space to the DAG space as an intra-batch strategy, then incorporates two RL agents—state-specific and state-invariant—to uncover causal relationships and integrates these agents into an incremental learning framework. Furthermore, the framework leverages a factored action space to enhance parallelization efficiency. Extensive experiments on synthetic and real datasets demonstrate that MARLIN outperforms state-of-the-art methods in terms of both efficiency and effectiveness. 

Online Anomaly Detection (OAD) is critical for identifying rare yet important data points in large, dynamic, and complex data streams. A key challenge lies in achieving accurate and consistent detection of anomalies while maintaining computational and memory efficiency. Conventional OAD approaches, which depend on distributional deviations and static thresholds, struggle with model update delays and catastrophic forgetting, leading to missed detections and high false positive rates. To address these limitations, we propose a novel Streaming Anomaly Detection (SAD) method, grounded in a sparse active online learning framework. Our approach uniquely integrates ℓ1,2-norm sparse online learning with CUR decomposition-based active learning, enabling simultaneous fast feature selection and dynamic instance selection. The efficient CUR decomposition further supports real-time residual analysis for anomaly scoring, eliminating the need for manual threshold settings about temporal data distributions. Extensive experiments on diverse streaming datasets demonstrate SAD's superiority, achieving a 14.06% reduction in detection error rates compared to five state-of-the-art competitors.