Child safety continues to be a paramount concern worldwide, with child abduction posing significant threats to communities. This paper presents the development of an edge-based child abduction detection and alert system utilizing a multi-agent framework where each agent incorporates Vision-Language Models (VLMs) deployed on a Raspberry Pi. Leveraging the advanced capabilities of VLMs within individual agents of a multi-agent team, our system is trained to accurately detect and interpret complex interactions involving children in various environments in real-time. The multi-agent system is deployed on a Raspberry Pi connected to a webcam, forming an edge device capable of processing video feeds, thereby reducing latency and enhancing privacy. An integrated alert system utilizes the Twilio API to send immediate SMS and WhatsApp notifications, including calls and messages, when a potential child abduction event is detected. Experimental results demonstrate that the system achieves high accuracy in detecting potential abduction scenarios, with near real-time performance suitable for practical deployment. The multi-agent architecture enhances the system's ability to process complex situational data, improving detection capabilities over traditional single-model approaches. The edge deployment ensures scalability and cost-effectiveness, making it accessible for widespread use. The proposed system offers a proactive solution to enhance child safety through continuous monitoring and rapid alerting, contributing a valuable tool in efforts to prevent child abductions.

In financial trading, large language model (LLM)-based agents demonstrate significant potential. However, the high sensitivity to market noise undermines the performance of LLM-based trading systems. To address this limitation, we propose a novel multi-agent system featuring an internal competitive mechanism inspired by modern corporate management structures. The system consists of two specialized teams: (1) Data Team - responsible for processing and condensing massive market data into diversified text factors, ensuring they fit the model's constrained context. (2) Research Team - tasked with making parallelized multipath trading decisions based on deep research methods. The core innovation lies in implementing a real-time evaluation and ranking mechanism within each team, driven by authentic market feedback. Each agent's performance undergoes continuous scoring and ranking, with only outputs from top-performing agents being adopted. The design enables the system to adaptively adjust to dynamic environment, enhances robustness against market noise and ultimately delivers superior trading performance. Experimental results demonstrate that our proposed system significantly outperforms prevailing multi-agent systems and traditional quantitative investment methods across diverse evaluation metrics. ContestTrade is open-sourced on GitHub at https://github.com/FinStep-AI/ContestTrade.

With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. T o facilitate research in this area, we organized the End-to-End Autonomous Driving through V2X Cooperation Challenge, which features two tracks: cooperative temporal perception and cooperative end-to-end planning. Built on the UniV2X framework and the V2X-Seq-SPD dataset, the challenge attracted participation from over 30 teams worldwide and established a unified benchmark for evaluating cooperative driving systems. This paper describes the design and outcomes of the challenge, highlights key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration, and analyzes emerging technical trends among top-performing solutions. By addressing practical constraints in communication and data fusion, the challenge contributes to the development of scalable and reliable V2X-cooperative autonomous driving systems.

Vision-language model-based mobile agents have gained the ability to not only understand complex instructions and mobile screenshots, but also optimize their action outputs via thinking and reasoning, benefiting from reinforcement learning, such as Group Relative Policy Optimization (GRPO). However, existing research centers on offline reinforcement learning training or online optimization using action-level rewards, which limits the agent's dynamic interaction with the environment. This often results in agents settling into local optima, thereby weakening their ability for exploration and error action correction. To address these challenges, we introduce an approach called Mobile-R1, which employs interactive multi-turn reinforcement learning with task-level rewards for mobile agents. Our training framework consists of three stages: initial format finetuning, single-step online training via action-level reward, followed by online training via task-level reward based on multi-turn trajectories. This strategy is designed to enhance the exploration and error correction capabilities of Mobile-R1, leading to significant performance improvements. Moreover, we have collected a dataset covering 28 Chinese applications with 24,521 high-quality manual annotations and established a new benchmark with 500 trajectories.

Explainable AI (XAI) systems have been proposed to help people understand how AI systems produce outputs and behaviors. Explainable Reinforcement Learning (XRL) has an added complexity due to the temporal nature of sequential decision-making. Further, non-AI experts do not necessarily have the ability to alter an agent or its policy. We introduce a technique for using World Models to generate explanations for Model-Based Deep RL agents. World Models predict how the world will change when actions are performed, allowing for the generation of counterfactual trajectories. However, identifying what a user wanted the agent to do is not enough to understand why the agent did something else. We augment Model-Based RL agents with a Reverse World Model, which predicts what the state of the world should have been for the agent to prefer a given counterfactual action. We show that explanations that show users what the world should have been like significantly increase their understanding of the agent's policy. We hypothesize that our explanations can help users learn how to control the agent's execution through manipulating the environment.

V ehicles are spawned in CARLA via SUMO, and managed by the Traffic Manager. We employ the dataset format of the nuScenes and extend it to multi-agent scenarios, seen in Fig. IV. Each log file can produce 100 scenes, and each scene includes 100 frames. The input BEV map's dimension is (c, w,h) = (13, 256, 256). II.1 Architecture of student/teacher encoder We describe the architecture of the encoder below.