Extensive research has investigated the integration of large language models (LLMs) with knowledge graphs to enhance the reasoning process. However, understanding how models perform reasoning utilizing structured graph knowledge remains underexplored. Most existing approaches rely on LLMs or retrievers to make binary judgments regarding the utilization of knowledge, which is too coarse. Meanwhile, there is still a lack of feedback mechanisms for reflection and correction throughout the entire reasoning path. This paper proposes an Active self-Reflection framework for knowledge Graph reasoning ARG, introducing for the first time an end-to-end training approach to achieve iterative reasoning grounded on structured graphs. Within the framework, the model leverages special tokens to \textit{actively} determine whether knowledge retrieval is necessary, performs \textit{reflective} critique based on the retrieved knowledge, and iteratively reasons over the knowledge graph. The reasoning paths generated by the model exhibit high interpretability, enabling deeper exploration of the model's understanding of structured knowledge. Ultimately, the proposed model achieves outstanding results compared to existing baselines in knowledge graph reasoning tasks.

As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.

Soft, growing inflated beam robots, also known as everting vine robots, have previously been shown to navigate confined spaces with ease. Less is known about their ability to navigate three-dimensional open spaces where they have the potential to collapse under their own weight as they attempt to move through a space. Previous work has studied collapse of inflated beams and vine robots due to purely transverse or purely axial external loads. Here, we extend previous models to predict the length at which straight vine robots will collapse under their own weight at arbitrary launch angle relative to gravity, inflated diameter, and internal pressure. Our model successfully predicts the general trends of collapse behavior of straight vine robots. We find that collapse length increases non-linearly with the robot's launch angle magnitude, linearly with the robot's diameter, and with the square root of the robot's internal pressure. We also demonstrate the use of our model to determine the robot parameters required to grow a vine robot across a gap in the floor. This work forms the foundation of an approach for modeling the collapse of vine robots and inflated beams in arbitrary shapes.

Ford plans to double its mobile connectivity engineering team by hiring 400 software developers and hardware engineers. Three-quarters of the new engineers will be part of a Canadian research-and-development centre aimed at building out connected vehicle technology that will eventually let vehicles talk to each other – and the infrastructure around them. Ford is investing about $375 million to establish its new Ottawa Research and Engineering Centre. It is Ford's first Canadian R&D facility focused on connectivity research and advanced technology. The global connected car market will reach $131 billion by 2019, with an annual growth rate of 30% per year, according to Transparency Market Research.

TORONTO – Ford Motor Co. will hire approximately 400 employees from embattled communications company BlackBerry Ltd. as part of sizable new investments in Canada that include a connected-vehicle research center in Ottawa, the smartphone maker said Thursday. Ford said the Ottawa research center is part of a 500 million Canadian dollar ($376 million) investment. The company also plans to increase sustainability and fuel economy research at its Windsor and Oakville operations. The company said it will hire approximately 300 engineers in Canada and 100 additional hardware and software engineers in the U.S. to support the work of the Canadian team. The new Ottawa Research and Engineering Centre in Canada will focus on research and development across infotainment, in-vehicle modems, gateway modules, driver-assist features and autonomous vehicles, said Ford.