Leveraging large language models (LLMs) in traffic signal control (TSC) improves optimization efficiency and interpretability compared to traditional reinforcement learning (RL) methods. However, existing LLM-based approaches are limited by fixed time signal durations and are prone to hallucination errors, while RL methods lack robustness in signal timing decisions and suffer from poor generalization. To address these challenges, this paper proposes HeraldLight, a dual LLMs architecture enhanced by Herald guided prompts. The Herald Module extracts contextual information and forecasts queue lengths for each traffic phase based on real-time conditions. The first LLM, LLM-Agent, uses these forecasts to make fine grained traffic signal control, while the second LLM, LLM-Critic, refines LLM-Agent's outputs, correcting errors and hallucinations. These refined outputs are used for score-based fine-tuning to improve accuracy and robustness. Simulation experiments using CityFlow on real world datasets covering 224 intersections in Jinan (12), Hangzhou (16), and New York (196) demonstrate that HeraldLight outperforms state of the art baselines, achieving a 20.03% reduction in average travel time across all scenarios and a 10.74% reduction in average queue length on the Jinan and Hangzhou scenarios. The source code is available on GitHub: https://github.com/BUPT-ANTlab/HeraldLight.

Doctors are more like machines': my mother's worrying reliance on AI for health advice Tired of a two-day commute to see her overworked doctor, my mother turned to tech for help with her kidney disease. E very few months, my mother, a 57-year-old kidney transplant patient who lives in a small city in eastern China, embarks on a two-day journey to see her doctor. She fills her backpack with a change of clothes, a stack of medical reports and a few boiled eggs to snack on. Then, she takes a 90-minute ride on a high-speed train and checks into a hotel in the eastern metropolis of Hangzhou. At 7am the next day, she lines up with hundreds of others to get her blood taken in a long hospital hall that buzzes like a crowded marketplace. In the afternoon, when the lab results arrive, she makes her way to a specialist's clinic. She gets about three minutes with the doctor. Then, my mother packs up and starts the long commute home. My mother began using China's leading AI chatbot to diagnose her symptoms this past winter. She would lie down on her couch and open the app on her iPhone. "Hi," she said in her first message to the chatbot, on 2 February. How can I assist you today?" the system responded instantly, adding a smiley emoji.

The 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025) took place from October 19 to 25, 2025 in Hangzhou, China. The programme included plenary and keynote talks, workshops, tutorials, forums, competitions, and a debate. There was also an exhibition where companies and institutions were able to showcase their latest hardware and software. We cast an eye over the social media platforms to see what participants got up to during the week. Truly enjoyed discussing the consolidation of specialist and generalist approaches to physical AI at #IROS2025.

--Unmanned aerial vehicles (UA Vs) are increasingly employed in diverse applications such as land surveying, material transport, and environmental monitoring. Following missions like data collection or inspection, UA Vs must land safely at docking stations for storage or recharging, which is an essential requirement for ensuring operational continuity. However, accurate landing remains challenging due to factors like GPS signal interference. T o address this issue, we propose a deviation warning system for UA V landings, powered by a novel vision-based model called AeroLite-MDNet. This model integrates a multiscale fusion module for robust cross-scale object detection and incorporates a segmentation branch for efficient orientation estimation. We introduce a new evaluation metric, A verage Warning Delay (A WD), to quantify the system's sensitivity to landing deviations. Furthermore, we contribute a new dataset, UA VLand-Data, which captures real-world landing deviation scenarios to support training and evaluation. Experimental results show that our system achieves an A WD of 0.7 seconds with a deviation detection accuracy of 98.6%, demonstrating its effectiveness in enhancing UA V landing reliability. NMANNED aerial vehicles (UA Vs), also known as drones, have been widely used in fire detection, geological hazard monitoring, and dangerous behavior monitoring [1] for their agility, compactness, and cost-efficiency. To reduce the dependency of UA Vs on human labor and skills, UA V nests are widely used to minimize manual operations, allowing the UA Vs to perform autonomous monitoring. UA V nests also offer functionalities such as safe parking, charging, data transmission, routine maintenance, repairs, and communication relays [2].

This paper introduces an underwater multi-robot platform, named Aucamp, characterized by cost-effective monocular-camera-based sensing, distributed protocol and robust orientation control for localization. We utilize the clarity feature to measure the distance, present the monocular imaging model, and estimate the position of the target object. We achieve global positioning in our platform by designing a distributed update protocol. The distributed algorithm enables the perception process to simultaneously cover a broader range, and greatly improves the accuracy and robustness of the positioning. Moreover, the explicit dynamics model of the robot in our platform is obtained, based on which, we propose a robust orientation control framework. The control system ensures that the platform maintains a balanced posture for each robot, thereby ensuring the stability of the localization system. The platform can swiftly recover from an forced unstable state to a stable horizontal posture. Additionally, we conduct extensive experiments and application scenarios to evaluate the performance of our platform. The proposed new platform may provide support for extensive marine exploration by underwater sensor networks.

In a world where human boxers are at risk of dangerous injuries, we may have a glimpse of what the fight of the future could look like. New footage shows the world's first humanoid robot boxing tournament, which took place over the weekend in Hangzhou, east China. In the bizarre clip, two lifesize robots wearing gloves and protective headgear fight each other in a ring as a human officiator looks on. Each fighter robot weighs about 35kg and is 4.3ft (132cm) tall – roughly the height of the average eight-year-old child. Both the bots initially have trouble seeing exactly where their opponent is before successfully trading punches and kicks, to the delight of a baying crowd.

Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules--GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks--RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks.

The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that omit critical information. This deficiency makes it challenging for traditional legal question-answering systems to accurately identify users' actual needs, often resulting in imprecise or generalized advice. In this work, we develop a legal question-answering system called Intelligent Legal Assistant, which interacts with users to precisely capture their needs. When a user poses a question, the system requests that the user select their geographical location to pinpoint the applicable laws. It then generates clarifying questions and options based on the key information missing from the user's initial question. This allows the user to select and provide the necessary details. Once all necessary information is provided, the system produces an in-depth legal analysis encompassing three aspects: overall conclusion, jurisprudential analysis, and resolution suggestions.

We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news, etc.). Specifically, we design OneKE with multiple agents and a configure knowledge base. Different agents perform their respective roles, enabling support for various extraction scenarios. The configure knowledge base facilitates schema configuration, error case debugging and correction, further improving the performance. Empirical evaluations on benchmark datasets demonstrate OneKE's efficacy, while case studies further elucidate its adaptability to diverse tasks across multiple domains, highlighting its potential for broad applications. We have open-sourced the Code at https://github.com/zjunlp/OneKE and released a Video at http://oneke.openkg.cn/demo.mp4.

Kepler's Forerunner K2 represents the fifth generation of its humanoid robot technology. Footage from the streets of China captured a scene straight from a science fiction novel – spherical drones alongside patrolling law enforcement. Chinese robotics company, Logon Technology, unveiled the RT-G autonomous spherical robot in a release, saying it was a "technological breakthrough" designed to assist and even replace humans in dangerous environments. The spherical robots are capable of operating both on land and water. The robots can reach speeds of up to 35km/h (approximately 22 mph) and withstand impact damage of up to 8,818 pounds (4 tons), the company said.