AI Open最新文献

{"title":"Human professional level driving agent for race car simulation environments","authors":"Gergely Bári ,&nbsp;László Palkovics","doi":"10.1016/j.aiopen.2026.02.007","DOIUrl":"10.1016/j.aiopen.2026.02.007","url":null,"abstract":"<div><div>Precise vehicle control at the limits of tire adhesion is paramount for both competitive motorsport performance and the safe execution of emergency maneuvers in road vehicles. Mastering this “grip-limit driving” presents significant challenges due to highly non-linear vehicle dynamics and sensitivity to changing conditions, often exceeding the capabilities of traditional controllers and driver models. This paper investigates the efficacy of Deep Reinforcement Learning (DRL), specifically the Proximal Policy Optimisation (PPO) algorithm, as a data-driven approach to learn expert-level driving skills within the TORCS high-fidelity race car simulation environment. An agent was trained end-to-end, utilizing “realworld-friendly” state signals (such as speeds, accelerations, and yaw rate, simple LiDaR, etc.) as input to determine continuous steering and pedal commands. Notably, the trained Agent achieved lap times comparable to a human e-sport world champion on the target track, demonstrating the potential of this methodology while also highlighting how agents can exploit idealized simulation to achieve superhuman control. Furthermore, this work presents the formulation of the time-optimal driving task as a DRL problem and offers a novel justification for the commonly used “progress reward” function, demonstrating its conceptual link to the time-difference feedback mechanisms human drivers use for performance optimization. These findings provide valuable insights into AI-driven vehicle control under extreme conditions and contribute to the development of more capable autonomous agents for simulation and potentially, real-world applications.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 123-132"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147540173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems","authors":"Shaina Raza ,&nbsp;Ranjan Sapkota ,&nbsp;Manoj Karkee ,&nbsp;Christos Emmanouilidis","doi":"10.1016/j.aiopen.2026.02.006","DOIUrl":"10.1016/j.aiopen.2026.02.006","url":null,"abstract":"<div><div>Agentic AI systems, built upon large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligence, autonomy, collaboration, and decision-making across enterprise and societal domains. This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of LLM-based Agentic Multi-Agent Systems (AMAS). We begin by examining the conceptual foundations of Agentic AI and highlight its architectural distinctions from traditional AI agents. We then adapt and extend the AI TRiSM framework for Agentic AI, structured around key pillars: <em>Explainability, ModelOps, Security, Privacy</em> and <em>their Lifecycle Governance</em>, each contextualized to the challenges of AMAS. A risk taxonomy is proposed to capture the unique threats and vulnerabilities of Agentic AI, ranging from coordination failures to prompt-based adversarial manipulation. To make coordination and tool use measurable in practice, we propose two metrics: the Component Synergy Score (CSS), which captures inter-agent enablement, and the Tool Utilization Efficacy (TUE), which evaluates whether tools are invoked correctly and efficiently. We further discuss strategies for improving explainability in Agentic AI, as well as approaches to enhancing security and privacy through encryption, adversarial robustness, and regulatory compliance. The review concludes with a research roadmap for the responsible development and deployment of Agentic AI, highlighting key directions to align emerging systems with TRiSM principles-ensuring safety, transparency, and accountability in their operation.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 71-95"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147404227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GHOST 2.0: Generative high-fidelity one shot transfer of heads","authors":"Alexander Groshev ,&nbsp;Anastasiia Iashchenko ,&nbsp;Pavel Paramonov ,&nbsp;Denis Dimitrov ,&nbsp;Andrey Kuznetsov","doi":"10.1016/j.aiopen.2026.02.003","DOIUrl":"10.1016/j.aiopen.2026.02.003","url":null,"abstract":"<div><div>While the task of face swapping has recently gained attention in the research community, a related problem of head swapping remains largely unexplored. In addition to skin color transfer, head swap poses extra challenges, such as the need to preserve structural information of the whole head during synthesis and inpaint gaps between swapped head and background. In this paper, we address these concerns with GHOST 2.0, which consists of two problem-specific modules. First, we introduce enhanced Aligner model for head reenactment, which preserves identity information at multiple scales and is robust to extreme pose variations. Secondly, we use a Blender module that seamlessly integrates the reenacted head into the target background by transferring skin color and inpainting mismatched regions. Both modules outperform the baselines on the corresponding tasks, allowing to achieve state-of-the-art results in head swapping. We also tackle complex cases, such as large difference in hair styles of source and target.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 45-61"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147404237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating cross-view multi-scale perception and RAG-enabled expert fusion for medical prediction","authors":"Cheng Wang,&nbsp;Yongbin Liu,&nbsp;Ying Yu,&nbsp;Chunping Ouyang,&nbsp;Yaping Wan","doi":"10.1016/j.aiopen.2026.02.004","DOIUrl":"10.1016/j.aiopen.2026.02.004","url":null,"abstract":"<div><div>Electronic Health Records (EHRs) continuously monitor patients’ health status in Intensive Care Units (ICUs), capturing irregular numerical time-series data and unstructured clinical text. While existing studies primarily focus on handling modality irregularities, they often overlook the complex intra- and inter-sequence interactions as well as the dependencies between short-term and long-term features. Moreover, clinical notes are typically semantically sparse and structurally noisy, making them difficult to interpret. To address these challenges, we propose a novel multimodal predictive model. For irregular numerical time-series data, we design a cross-view multi-scale framework that integrates cross-attention mechanisms with multi-scale convolutions. This enables dynamic modeling of diverse temporal embeddings while precisely capturing intrinsic inter-variable interactions and cross-temporal dependencies, all with reduced computational complexity. For clinical text, we adopt a retrieval-augmented technique that leverages external medical knowledge graphs (KGs) and large language models (LLMs) to enrich text representations related to medical codes. These enhanced embeddings are then fused with clinical notes via a gated mechanism, effectively alleviating semantic sparsity. We validate the effectiveness of the proposed approach on two critical clinical prediction tasks. Experimental results show maximum relative F1 score improvements of 3.3%, 6.0%, and 3.4% for MISTS, clinical notes, and multimodal fusion tasks, respectively, demonstrating our method’s excellent medical predictive capability.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 62-70"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147404238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bio-inspired adaptive neurons for dynamic weighting in Artificial Neural Networks","authors":"Ashhadul Islam ,&nbsp;Abdesselam Bouzerdoum ,&nbsp;Samir Brahim Belhaouari","doi":"10.1016/j.aiopen.2026.02.001","DOIUrl":"10.1016/j.aiopen.2026.02.001","url":null,"abstract":"<div><div>Traditional neural networks employ fixed weights during inference, limiting their ability to adapt to changing input conditions, unlike biological neurons that adjust signal strength dynamically based on stimuli. This discrepancy between artificial and biological neurons constrains neural network flexibility and adaptability. To bridge this gap, we propose a novel framework for adaptive neural networks, where neuron weights are modeled as functions of the input signal, allowing the network to adjust dynamically in real-time. Importantly, we achieve this within the same traditional architecture of an Artificial Neural Network, maintaining structural familiarity while introducing dynamic adaptability. In our research, we apply Chebyshev polynomials as one of the many possible decomposition methods to achieve this adaptive weighting mechanism, with polynomial coefficients learned during training. Of the 145 datasets tested, our adaptive Chebyshev neural network demonstrated a marked improvement over an equivalent MLP in approximately 83% of the cases, performing strictly better on 121 datasets. In the remaining 24 datasets, the performance of our algorithm matched that of the MLP, highlighting its ability to generalize the behavior of standard neural networks while offering enhanced adaptability. As a generalized form of MLP, this model seamlessly retains MLP performance where needed while extending its capabilities to achieve superior accuracy across a wide range of complex tasks. These results underscore the potential of adaptive neurons to enhance generalization, flexibility, and robustness in neural networks, particularly in applications with dynamic or non-linear data dependencies.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 1-17"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147404235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not another imputation method: A transformer-based model for missing values in tabular datasets","authors":"Camillo Maria Caruso ,&nbsp;Paolo Soda ,&nbsp;Valerio Guarrasi","doi":"10.1016/j.aiopen.2026.02.005","DOIUrl":"10.1016/j.aiopen.2026.02.005","url":null,"abstract":"<div><div>Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation techniques. Here we introduce “Not Another Imputation Method” (NAIM), a novel transformer-based model specifically designed to address this issue without the need for traditional imputation techniques. NAIM’s ability to avoid the necessity of imputing missing values and to effectively learn from available data relies on two main techniques: the use of feature-specific embeddings to encode both categorical and numerical features also handling missing inputs; the modification of the masked self-attention mechanism to completely mask out the contributions of missing data. Additionally, a novel regularization technique is introduced to enhance the model’s generalization capability from incomplete data. We extensively evaluated NAIM on 5 publicly available tabular datasets, demonstrating its superior performance over 6 state-of-the-art machine learning models and 5 deep learning models, each paired with 3 different imputation techniques when necessary. The results highlight the efficacy of NAIM in improving predictive performance and resilience in the presence of missing data. To facilitate further research and practical application in handling missing data without traditional imputation methods, we made the code for NAIM available at <span><span>https://github.com/cosbidev/NAIM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 96-122"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147449778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who is the most suitable one? Compliance review method based on multi-agent routing","authors":"Chutian Yu ,&nbsp;Jiangqian Huang ,&nbsp;Xin Chen ,&nbsp;Meijin Gao ,&nbsp;Lijun Zhang ,&nbsp;Xiang Yuan ,&nbsp;Junjie Sun ,&nbsp;Dezheng Bao ,&nbsp;Yang Yang","doi":"10.1016/j.aiopen.2026.03.001","DOIUrl":"10.1016/j.aiopen.2026.03.001","url":null,"abstract":"<div><div>Project compliance review serves as a critical component in ensuring that project submissions meet regulatory and procedural standards while filtering out non-compliant proposals. However, the rapid increase in project applications has rendered traditional manual review mechanisms inefficient and unsustainable. Existing automated approaches—based on either semantic vector matching or direct use of large language models (LLMs)—often struggle with heterogeneous document structures, limited robustness, and high computational cost. To overcome these challenges, this study proposes an automated compliance review framework based on <strong>multi-agent routing</strong>. The framework integrates multiple expert agents with distinct reasoning paradigms and employs a classification agent to dynamically route each task to the most suitable expert, thereby enabling data-adaptive decision-making. Through adaptive routing, the system effectively reduces redundant computation by invoking complex reasoning only when necessary. Experimental results on real-world datasets from the power industry demonstrate that our method achieves superior accuracy and <strong>48% reduction in token consumption</strong> compared to the best-performing single-agent baseline, achieving a balanced trade-off between effectiveness and efficiency. Furthermore, an online review platform has been developed and successfully deployed in large-scale power project evaluation scenarios, validating the practicality and scalability of the proposed approach.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 133-141"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147740310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge intensive agents","authors":"Zhenghao Liu ,&nbsp;Pengcheng Huang ,&nbsp;Zhipeng Xu ,&nbsp;Xinze Li ,&nbsp;Shuliang Liu ,&nbsp;Chunyi Peng ,&nbsp;Haidong Xin ,&nbsp;Yukun Yan ,&nbsp;Shuo Wang ,&nbsp;Xu Han ,&nbsp;Zhiyuan Liu ,&nbsp;Maosong Sun ,&nbsp;Yu Gu ,&nbsp;Ge Yu","doi":"10.1016/j.aiopen.2026.02.002","DOIUrl":"10.1016/j.aiopen.2026.02.002","url":null,"abstract":"<div><div>Large Language Models (LLMs) have exhibited impressive capabilities in reasoning and language understanding. However, their reliance on memorized knowledge and tendency to generate hallucinated content limit their reliability in real-world applications. Retrieval-Augmented Generation (RAG) mitigates these issues by integrating a retrieval module that supplements LLMs with relevant external knowledge. This paradigm bridges parametric memory and explicit retrieval, offering a principled way to ground generation in factual evidence. Despite substantial progress, most prior work has focused on optimizing isolated components, either retrieval or generation, while overlooking the agentic perspective, in which LLMs act as autonomous agents capable of actively acquiring and strategically utilizing knowledge. In this perspectives paper, we argue for reinterpreting RAG as a collaborative knowledge process among agents with distinct yet complementary roles. We categorize knowledge-intensive agents into two primary roles: knowledge acquisition (e.g., routing, query reformulation) and knowledge utilization (e.g., knowledge refinement, response generation). From this viewpoint, RAG becomes a dynamic system in which knowledge is continuously transmitted, transformed, and aligned across agent roles. To fully realize this paradigm, we advocate a joint optimization framework for knowledge-intensive agents within RAG systems. This framework explicitly models the dynamics of knowledge flow in multi-agent settings, aligning knowledge supply with knowledge demand through LLM-driven data synthesis, feedback, and evaluation. By fostering adaptive and targeted knowledge exchange, the framework mitigates conflicts between parametric and retrieved knowledge, thereby enhancing both coherence and factuality. We argue that this multi-agent joint optimization paradigm improves RAG systems in scalability, reliability, and adaptability, unlocking the potential for next-generation knowledge-intensive LLMs that reason, retrieve, and collaborate across deep retrieval processes and diverse vertical domains.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 18-44"},"PeriodicalIF":14.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147404236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SafeCast: Risk-responsive motion forecasting for autonomous vehicles","authors":"Haicheng Liao ,&nbsp;Hanlin Kong ,&nbsp;Zhenning Li ,&nbsp;Chengzhong Xu","doi":"10.1016/j.aiopen.2025.08.001","DOIUrl":"10.1016/j.aiopen.2025.08.001","url":null,"abstract":"<div><div>Accurate motion forecasting is essential for the safety and reliability of autonomous driving (AD) systems. While existing methods have made significant progress, they often overlook explicit safety constraints and struggle to capture the complex interactions among traffic agents, environmental factors, and motion dynamics. To address these challenges, we present SafeCast, a risk-responsive motion forecasting model that integrates safety-aware decision-making with uncertainty-aware adaptability. SafeCast is the first to incorporate the Responsibility-Sensitive Safety (RSS) framework into motion forecasting, encoding interpretable safety rules — such as safe distances and collision avoidance — based on traffic norms and physical principles. To further enhance robustness, we introduce the Graph Uncertainty Feature (GUF), a graph-based module that injects learnable noise into Graph Attention Networks, capturing real-world uncertainties and enhancing generalization across diverse scenarios. We evaluate SafeCast on four real-world benchmark datasets — Next Generation Simulation (NGSIM), Highway Drone (HighD), ApolloScape, and the Macao Connected Autonomous Driving (MoCAD) — covering highway, urban, and mixed-autonomy traffic environments. Our model achieves state-of-the-art (SOTA) accuracy while maintaining a lightweight architecture and low inference latency, underscoring its potential for real-time deployment in safety-critical AD systems.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 118-129"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-generated content in landscape architecture: A survey","authors":"Yue Xing ,&nbsp;Wensheng Gan ,&nbsp;Qidi Chen ,&nbsp;Philip S. Yu","doi":"10.1016/j.aiopen.2025.10.002","DOIUrl":"10.1016/j.aiopen.2025.10.002","url":null,"abstract":"<div><div>Landscape design is a complex process that requires designers to engage in intricate planning, analysis, and decision-making. This process involves the integration and reconstruction of science, art, and technology. Traditional landscape design methods are shaped by various factors, including the designer’s knowledge, time constraints, local ecological climate, available resources, and environmental considerations. These methods often rely on the designer’s personal experience and subjective aesthetics, with design standards rooted in subjective perception. As a result, they lack scientific and objective evaluation criteria and systematic design processes. Data-driven artificial intelligence (AI) technology provides an objective and rational design process. With the rapid development of different AI technologies, AI-generated content (AIGC) has permeated various aspects of landscape design at an unprecedented speed, serving as an innovative design tool. This article aims to explore the applications and opportunities of AIGC in landscape design. AIGC can support landscape design in areas such as site research and analysis, design concepts and scheme generation, parametric design optimization, plant selection and visual simulation, construction management, and process optimization. However, AIGC also faces challenges in landscape design, including data quality and reliability, design expertise and judgment, technical challenges and limitations, site characteristics and sustainability, user needs and participation, the balance between technology and creativity, ethics, and social impact. Finally, this article provides a detailed outlook on the future development trends and prospects of AIGC in landscape design. Through in-depth research and exploration in this review, readers can gain a better understanding of the relevant applications, potential opportunities, and key challenges of AIGC in landscape design.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 220-243"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}