Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies

Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes. Although current medical AI can detect disease, estimate risk and generate reports, many systems still return static labels or scores, offering limited insight into how illness may progress or how alternative interventions may reshape its trajectory. Medical world models adapt the world-model idea from artificial intelligence to healthcare by learning internal simulators of patient-state dynamics. Their long-term goal is to help clinicians anticipate deterioration, compare treatment-conditioned futures and tailor care to individual patients. Yet relevant work remains scattered across foundation models, longitudinal modelling, disease simulation, treatment-effect estimation, reinforcement learning and digital twins. To bridge this gap, this review outlines a roadmap for advancing medical AI from isolated diagnosis and prediction toward medical world models that simulate disease evolution and support intervention decisions. This roadmap is organized around three coupled capabilities: patient-state construction, clinical dynamics modelling and intervention decision support. Across representative systems, the comparison highlights what each capability contributes and how partial components can be integrated into more mature perception--dynamics--planning systems. Finally, we identify the challenges involved in turning plausible rollouts into clinically useful simulators. Related literature is available at https://github.com/1999kevin/awesome_medical_world_models.

EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning

Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods typically rely on fixed thresholds or perform clustering with a fixed number of clusters to distinguish malicious gradients from benign ones. However, these methods are difficult to adapt to dynamic poisoning strategies of malicious clients, and often result in the loss of benign gradients due to the heterogeneity of clients' local datasets. To address these problems, we propose a novel robust aggregation method that leverages a small number of known benign clients as references, enabling accurate identification and filtering of malicious gradients while retaining as many benign gradients as possible, even when the number of malicious clients is unknown and variable. First, we introduce a density-based low-dimensional gradient clustering method, which projects gradients onto the two most divergent dimensions and applies density-based clustering to identify malicious gradients while retaining clustered benign gradients and potentially benign outliers. Second, we design an enhancing clustering low-dimensional gradient generator model, which learns to generate pseudo-gradients aligned with the boundary of the benign cluster. These pseudo-gradients act as bridges to connect sparse benign gradient outliers. Third, we introduce low-dimensional gradient re-clustering that clusters the generated pseudo-gradients together with real gradients to recover benign gradients misclassified as noise points, enabling more benign gradients to participate in aggregation. Extensive experiments on the MNIST, CIFAR-10, and MIND datasets demonstrate that our method exhibits superior fidelity and robustness under dynamic poisoning scenarios.

Unsupervised Multi-agent and Single-agent Perception from Cooperative Views

The LiDAR-based multi-agent and single-agent perception has shown promising performance in environmental understanding for robots and automated vehicles. However, there is no existing method that simultaneously solves both multi-agent and single-agent perception in an unsupervised way. By sharing sensor data between multiple agents via communication, this paper discovers two key insights: 1) Improved point cloud density after the data sharing from cooperative views could benefit unsupervised object classification, 2) Cooperative view of multiple agents can be used as unsupervised guidance for the 3D object detection in the single view. Based on these two discovered insights, we propose an Unsupervised Multi-agent and Single-agent (UMS) perception framework that leverages multi-agent cooperation without human annotations to simultaneously solve multi-agent and single-agent perception. UMS combines a learning-based Proposal Purifying Filter to better classify the candidate proposals after multi-agent point cloud density cooperation, followed by a Progressive Proposal Stabilizing module to yield reliable pseudo labels by the easy-to-hard curriculum learning. Furthermore, we design a Cross-View Consensus Learning to use multi-agent cooperative view to guide detection in single-agent view. Experimental results on two public datasets V2V4Real and OPV2V show that our UMS method achieved significantly higher 3D detection performance than the state-of-the-art methods on both multi-agent and single-agent perception tasks in an unsupervised setting.

DarkDriving: A Real-World Day and Night Aligned Dataset for Autonomous Driving in the Dark Environment

The low-light conditions are challenging to the vision-centric perception systems for autonomous driving in the dark environment. In this paper, we propose a new benchmark dataset (named DarkDriving) to investigate the low-light enhancement for autonomous driving. The existing real-world low-light enhancement benchmark datasets can be collected by controlling various exposures only in small-ranges and static scenes. The dark images of the current nighttime driving datasets do not have the precisely aligned daytime counterparts. The extreme difficulty to collect a real-world day and night aligned dataset in the dynamic driving scenes significantly limited the research in this area. With a proposed automatic day-night Trajectory Tracking based Pose Matching (TTPM) method in a large real-world closed driving test field (area: 69 acres), we collected the first real-world day and night aligned dataset for autonomous driving in the dark environment. The DarkDriving dataset has 9,538 day and night image pairs precisely aligned in location and spatial contents, whose alignment error is in just several centimeters. For each pair, we also manually label the object 2D bounding boxes. DarkDriving introduces four perception related tasks, including low-light enhancement, generalized low-light enhancement, and low-light enhancement for 2D detection and 3D detection of autonomous driving in the dark environment. The experimental results show that our DarkDriving dataset provides a comprehensive benchmark for evaluating low-light enhancement for autonomous driving and it can also be generalized to enhance dark images and promote detection in some other low-light driving environment, such as nuScenes.

A Low-Rank Method for Vision Language Model Hallucination Mitigation in Autonomous Driving

Vision Language Models (VLMs) are increasingly used in autonomous driving to help understand traffic scenes, but they sometimes produce hallucinations, which are false details not grounded in the visual input. Detecting and mitigating hallucinations is challenging when ground-truth references are unavailable and model internals are inaccessible. This paper proposes a novel self-contained low-rank approach to automatically rank multiple candidate captions generated by multiple VLMs based on their hallucination levels, using only the captions themselves without requiring external references or model access. By constructing a sentence-embedding matrix and decomposing it into a low-rank consensus component and a sparse residual, we use the residual magnitude to rank captions: selecting the one with the smallest residual as the most hallucination-free. Experiments on the NuScenes dataset demonstrate that our approach achieves 87% selection accuracy in identifying hallucination-free captions, representing a 19% improvement over the unfiltered baseline and a 6-10% improvement over multi-agent debate method. The sorting produced by sparse error magnitudes shows strong correlation with human judgments of hallucinations, validating our scoring mechanism. Additionally, our method, which can be easily parallelized, reduces inference time by 51-67% compared to debate approaches, making it practical for real-time autonomous driving applications.

PFedDST: Personalized Federated Learning with Decentralized Selection Training

Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability disparities, which can impede training efficiency. Communication bottlenecks further complicate traditional Federated Learning (FL) setups. To mitigate these issues, we introduce the Personalized Federated Learning with Decentralized Selection Training (PFedDST) framework. PFedDST enhances model training by allowing devices to strategically evaluate and select peers based on a comprehensive communication score. This score integrates loss, task similarity, and selection frequency, ensuring optimal peer connections. This selection strategy is tailored to increase local personalization and promote beneficial peer collaborations to strengthen the stability and efficiency of the training process. Our experiments demonstrate that PFedDST not only enhances model accuracy but also accelerates convergence. This approach outperforms state-of-the-art methods in handling data heterogeneity, delivering both faster and more effective training in diverse and decentralized systems.

Stealthy Multi-Task Adversarial Attacks

Deep Neural Networks exhibit inherent vulnerabilities to adversarial attacks, which can significantly compromise their outputs and reliability. While existing research primarily focuses on attacking single-task scenarios or indiscriminately targeting all tasks in multi-task environments, we investigate selectively targeting one task while preserving performance in others within a multi-task framework. This approach is motivated by varying security priorities among tasks in real-world applications, such as autonomous driving, where misinterpreting critical objects (e.g., signs, traffic lights) poses a greater security risk than minor depth miscalculations. Consequently, attackers may hope to target security-sensitive tasks while avoiding non-critical tasks from being compromised, thus evading being detected before compromising crucial functions. In this paper, we propose a method for the stealthy multi-task attack framework that utilizes multiple algorithms to inject imperceptible noise into the input. This novel method demonstrates remarkable efficacy in compromising the target task while simultaneously maintaining or even enhancing performance across non-targeted tasks - a criterion hitherto unexplored in the field. Additionally, we introduce an automated approach for searching the weighting factors in the loss function, further enhancing attack efficiency. Experimental results validate our framework's ability to successfully attack the target task while preserving the performance of non-targeted tasks. The automated loss function weight searching method demonstrates comparable efficacy to manual tuning, establishing a state-of-the-art multi-task attack framework.

LICM: Effective and Efficient Long Interest Chain Modeling for News Recommendation

Accurately recommending personalized candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interests to match candidate news. Recent efforts have primarily focused on extract local subgraph information, the lack of a comprehensive global news graph extraction has hindered the ability to utilize global news information collaboratively among similar users. To overcome these limitations, we propose an effective and efficient Long Interest Chain Modeling for News Recommendation(LICM), which combines neighbor interest with long-chain interest distilled from a global news click graph based on the collaborative of similar users to enhance news recommendation. For a global news graph based on the click history of all users, long chain interest generated from it can better utilize the high-dimensional information within it, enhancing the effectiveness of collaborative recommendations. We therefore design a comprehensive selection mechanism and interest encoder to obtain long-chain interest from the global graph. Finally, we use a gated network to integrate long-chain information with neighbor information to achieve the final user representation. Experiment results on real-world datasets validate the effectiveness and efficiency of our model to improve the performance of news recommendation.

EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV

Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies show that adding an adversarial patch on objects can fool the well-trained deep neural networks based object detectors, posing security concerns to the downstream tasks. However, the current public UAV datasets might ignore the diverse altitudes, vehicle attributes, fine-grained instance-level annotation in mostly side view with blurred vehicle roof, so none of them is good to study the adversarial patch based vehicle detection attack problem. In this paper, we propose a new dataset named EVD4UAV as an altitude-sensitive benchmark to evade vehicle detection in UAV with 6,284 images and 90,886 fine-grained annotated vehicles. The EVD4UAV dataset has diverse altitudes (50m, 70m, 90m), vehicle attributes (color, type), fine-grained annotation (horizontal and rotated bounding boxes, instance-level mask) in top view with clear vehicle roof. One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV. The experimental results show that these representative attack methods could not achieve the robust altitude-insensitive attack performance.

DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Macular Hole Reconstruction with Stochastic Retinal Defect Augmentation and Dynamic Weight Composition

The spatial and quantitative parameters of macular holes are vital for diagnosis, surgical choices, and post-op monitoring. Macular hole diagnosis and treatment rely heavily on spatial and quantitative data, yet the scarcity of such data has impeded the progress of deep learning techniques for effective segmentation and real-time 3D reconstruction. To address this challenge, we assembled the world's largest macular hole dataset, Retinal OCTfor Macular Hole Enhancement (ROME-3914), and a Comprehensive Archive for Retinal Segmentation (CARS-30k), both expertly annotated. In addition, we developed an innovative 3D segmentation network, the Dual-Encoder FuGH Network (DEFN), which integrates three innovative modules: Fourier Group Harmonics (FuGH), Simplified 3D Spatial Attention (S3DSA) and Harmonic Squeeze-and-Excitation Module (HSE). These three modules synergistically filter noise, reduce computational complexity, emphasize detailed features, and enhance the network's representation ability. We also proposed a novel data augmentation method, Stochastic Retinal Defect Injection (SRDI), and a network optimization strategy DynamicWeightCompose (DWC), to further improve the performance of DEFN. Compared with 13 baselines, our DEFN shows the best performance. We also offer precise 3D retinal reconstruction and quantitative metrics, bringing revolutionary diagnostic and therapeutic decision-making tools for ophthalmologists, and is expected to completely reshape the diagnosis and treatment patterns of difficult-to-treat macular degeneration. The source code is publicly available at: https://github.com/IIPL-HangzhouDianUniversity/DEFN-Pytorch.