On behalf of the ICTAI-2025 chairs and organizers, we would like to welcome you to the 37th International IEEE Conference on Tools with Artificial Intelligence (ICTAI-2025). For 37 years ICTAI has served as the first of its kind major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies. The conference facilitates the cross-fertilization of these ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications. ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI-based components of computer tools such as algorithms, architectures and languages.
Recent advancements in Neural Machine Translation (NMT) have significantly improved translation quality. However, the increasing size and complexity of state-of-the-art models present significant challenges for deployment on resource-limited devices. Knowledge distillation (KD) is a promising approach for compressing models, but its effectiveness diminishes when there is a large capacity gap between teacher and student models. To address this issue, we propose Evolving Knowledge Distillation (EKD), a progressive training framework in which the student model learns from a sequence of teachers with gradually increasing capacities. Experiments on IWSLT14, WMT-17, and WMT-23 benchmarks show that EKD leads to consistent improvements at each stage. On IWSLT-14, the final student achieves a BLEU score of 34.24, narrowing the gap to the strongest teacher (34.32 BLEU) to just 0.08 BLEU. Similar trends are observed on other datasets. These results demonstrate that EKD effectively bridges the capacity gap, enabling compact models to achieve performance close to that of much larger teacher models.Code and models are available at https://github.com/agi-content-generation/EKD.
In recent years, implicit neural representation of 3D scenes has evolved significantly and has been rapidly extended to multiple application scenarios. However, using this type of approach for highly dynamic urban scenes remains a challenging problem. Typical images of objects captured by cameras on board autonomous vehicles from one trajectory contains limited number of views, leading unsatisfactory reconstruction quality. To address this problem, we propose a novel hybrid network structure that decomposes the urban scene into a static background and multiple dynamic objects. The background model outputs a signed distance field with its accuracy enhanced by surface constraints. To deal with the issues of insufficient camera view we use a pre-training strategy to leverage shape and appearance prior to the object model from external datasets. We design an autoencoder architecture to encode a category of objects and employ an attention-based fusion module to better extract features from multiple object images. Furthermore, we introduce a “symmetric completion” approach to leverage the inherent symmetry property of normal cars. During experiments with data from Carla Platform, we find that our model can reconstruct scenes with high-fidelity, generate novel-view successfully and edit 3D scene freely.
This work advances existing research on converting OpenAPI descriptions into ontology-based semantic descriptions by implementing an improved ontology and translation method. This updated approach efficiently maps complex Schema objects and newly introduced asynchronous features proposed in the most recent OpenAPI version like Webhooks and Callbacks. The ontology has been evaluated through a dual assessment approach: a qualitative analysis ensuring structural and semantic integrity, and a quantitative test validating real-time efficiency in ontology mapping and query searches on a triple-store repository of approximately 10,000 OpenAPI descriptions sourced from the Swagger (API) Hub.
For over a decade, the iconic Super Mario Bros. game has been used as a benchmark for research in artificial intelligence (AI) and procedural content generation (PCG). Every PCG technique relies on some sort of level validation to ensure that the generated levels are playable. For this, an artificial agent is used to substitute for a human player. The quality of such an agent directly influences the quality of the work built upon it, as it limits the complexity of levels that can be validated, and its performance affects the size of the generative space that can be explored. In this paper, we present a new Super Mario Bros. agent, which first finds a coarse path over the grid-based abstraction of a game level, and then uses the information to guide an A* search through the simulated states of the game. The proposed agent is the first agent that is able to solve all levels with standard Super Mario Bros. features, while also being the most performant (10x fewer node evaluations needed and 8x faster on the most complex level pack), thus it constitutes a new state-of-the-art for the game and should be used as the new level validation standard. Furthermore, we empirically show that the grid-based search alone can be used for the level validation task with almost the same accuracy as the agent while being faster by two orders of magnitude.
