AI Open最新文献

{"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":"AgenticFedLock: Trust-conditioned multi-role agentic control for robust and communication-efficient federated learning","authors":"Habib Ullah Manzoor,&nbsp;Sanaullah Manzoor,&nbsp;Kamran Arshad,&nbsp;Khaled Assaleh,&nbsp;Muhammad Imran,&nbsp;Ahmed Zoha","doi":"10.1016/j.aiopen.2026.09.002","DOIUrl":"10.1016/j.aiopen.2026.09.002","url":null,"abstract":"<div><div>Federated learning (FL) enables collaborative training across decentralized devices, but under non-IID data it faces a persistent tension between adversarial robustness, communication efficiency, and utility on rare classes. We present AgenticFedLock, a trust-conditioned FL framework that treats robustness as sequential control of trainable parameter-space geometry through a hierarchical server-side protocol controller. Its four coordinated agents estimate client state, select participants, allocate trainable subspaces, and evaluate or repair risky updates. The Judge-Repair agent’s backdoor-risk signal is trigger-agnostic by construction: it fuses subspace-localized root-cosine deviation, trusted-consensus deviation, and norm-inflation detection, none of which use trigger or target information, combined with a norm-confidence-gated root-anchor reweighting at aggregation. This signal is verified to trigger repair in the large majority of completed rounds under an unknown-defender setting, with no dependence on privileged knowledge of the attack. Across a matched three-seed, five-method, four-dataset, two-attack unknown-defender comparison, the controller reduces model-replacement attack success to <span><math><mrow><mn>15.6</mn><mo>±</mo><mn>6.2</mn><mtext>%</mtext></mrow></math></span> on EMNIST Balanced and <span><math><mrow><mn>26.1</mn><mo>±</mo><mn>17.2</mn><mtext>%</mtext></mrow></math></span> on CIFAR-10, where FedAvg, FLTrust, FLAME, and RFA all remain above 90%; on MNIST and Fashion-MNIST it is competitive with the strongest baselines under both attacks studied. It does so while transmitting 60%–67% of protocol bytes against 100% for every full-model comparator. Beyond suppressing the attack’s effect, the same persistent per-client state lets the controller identify which clients are malicious with near-perfect accuracy under model replacement (suspicion-score AUC 0.999–1.000, reaching near-ceiling separation within the first few rounds), a capability the compared robust-aggregation baselines do not expose since none of them persist an identity-linked judgment across rounds; this attribution collapses to near-chance under the camouflaged adaptive-mimic attack. Matched ablation, sensitivity, defender-knowledge, and systems-cost results accompany these findings, including the cases where the mechanism does not close the gap.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 289-312"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"Privacy-preserving in-the-wild bodily expressed emotion recognition: A dual-teacher distillation framework with psychological priors","authors":"Shuang Wu,&nbsp;Daniela M. Romano","doi":"10.1016/j.aiopen.2026.08.001","DOIUrl":"10.1016/j.aiopen.2026.08.001","url":null,"abstract":"<div><div>Skeleton-based emotion recognition provides a privacy-friendly alternative to RGB-based affect analysis by reducing deployment-time exposure to facial appearance, texture, and scene context. However, models trained in controlled laboratory settings often degrade when deployed in the wild, where body motion is spontaneous, viewpoints and occlusions are unconstrained, and 2D poses are noisy. We address this lab-to-field gap with DT–KD, a structured supervision-transfer framework for in-the-wild bodily expressed emotion recognition. Rather than treating dual-teacher distillation as the contribution in itself, DT–KD uses two complementary teachers to target distinct sources of field degradation: a privileged 3D kinematic teacher provides clean motion guidance from laboratory data, while a modality-aligned 2D teacher provides supervision closer to the noisy skeleton-only deployment setting. Their guidance is fused through sample-wise entropy routing and further structured by a psychology-grounded <span><math><mrow><mn>7</mn><mo>→</mo><mn>26</mn></mrow></math></span> hierarchy, LMA/VAD prototypes, and lightweight feature-alignment and label-geometry regularizers. At deployment, only the compact student is retained, relying solely on 2D poses and 33-D Laban Movement Analysis (LMA) descriptors, with no RGB input or teacher model required.</div><div>We evaluate DT–KD on the in-the-wild BoLD benchmark with 26 affective classes. The distilled student achieves a macro ROC–AUC of 0.623 ± 0.011, improving over an LMA-only baseline by 8.8 percentage points and over a strong domain-adaptation baseline by 10.5 percentage points under the same inference budget of <span><math><mrow><mn>2.148</mn><mspace></mspace><mi>M</mi></mrow></math></span> parameters and <span><math><mrow><mn>0.136</mn><mspace></mspace><mi>G</mi></mrow></math></span> FLOPs per <span><math><mrow><mn>2</mn><mspace></mspace><mi>s</mi></mrow></math></span> clip. The gains persist under a strict cross-subject protocol and under synthetic pose occlusion and jitter, indicating improved robustness and calibration. We further assess external cross-dataset generalization on EMOTIC through family-level evaluation on the 7-way head, where DT–KD remains more discriminative and better calibrated under a severe shift from video clips to single-frame images. We also clarify that the proposed framework is privacy-preserving in a practical RGB-free inference sense, rather than a formal differential-privacy mechanism. Overall, entropy-routed structured supervision provides an effective and deployment-friendly strategy for narrowing the lab-to-field gap in skeleton-based emotion recognition under a fixed inference budget.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 248-275"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148843481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pioneering multimodal emotion recognition in the era of large models: Towards open vocabularies","authors":"Jing Han,&nbsp;Zhiqiang Gao,&nbsp;Shihao Gao,&nbsp;Jialing Liu,&nbsp;Hongyu Chen,&nbsp;Zixing Zhang,&nbsp;Björn W. Schuller","doi":"10.1016/j.aiopen.2026.06.001","DOIUrl":"10.1016/j.aiopen.2026.06.001","url":null,"abstract":"<div><div>Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable multi- and cross-modal integration capabilities. However, their potential for fine-grained emotion understanding remains systematically underexplored. While open-vocabulary multimodal emotion recognition (MER-OV) has emerged as a promising direction to overcome the limitations of closed emotion sets, no comprehensive evaluation of MLLMs in this context currently exists. To address this, our work presents the first large-scale benchmarking study of MER-OV on the OV-MERD dataset, evaluating <em>19 mainstream MLLMs</em>, including general-purpose, modality-specialized, and reasoning-enhanced architectures. Through systematic analysis of model reasoning capacity, fusion strategies, contextual utilization, and prompt design, we provide key insights into the capabilities and limitations of current MLLMs for MER-OV. Our evaluation reveals that a two-stage, trimodal (audio, video, and text) fusion approach achieves optimal performance in MER-OV, with video emerging as the most critical modality. We further identify a surprisingly narrow gap between open- and closed-source LLMs. These findings establish essential benchmarks and offer practical guidelines for advancing open-vocabulary and fine-grained affective computing, paving the way for more nuanced and interpretable emotion AI systems. Associated code will be made publicly available upon acceptance.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 ","pages":"Pages 194-207"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148776833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"FedGPA: Federated Learning with Global Personalized Aggregation","authors":"Zongfu Han ,&nbsp;Yu Feng ,&nbsp;Yifan Zhu ,&nbsp;Zhen Tian ,&nbsp;Fangyu Hao ,&nbsp;Meina Song","doi":"10.1016/j.aiopen.2025.03.001","DOIUrl":"10.1016/j.aiopen.2025.03.001","url":null,"abstract":"<div><div>A significant challenge in Federated Learning (FL) is addressing the heterogeneity of local data distribution across clients. Personalized Federated Learning (PFL), an emerging method aimed at overcoming data heterogeneity, can either integrate personalized components into the global model or train multiple models to achieve personalization. However, little research has simultaneously considered both directions. One such approach involves adopting a weighted aggregation method to generate personalized models, where the weights are determined by solving an optimization problem among different clients. In brief, previous works either neglect the use of global information during local representation learning or simply treat the personalized model as learning a set of individual weights. In this work, we initially decouple the model into a feature extractor, associated with generalization, and a classifier, linked to personalization. Subsequently, we conduct local–global alignment based on prototypes to leverage global information for learning better representations. Moreover, we fully utilize these representations to calculate the distance between clients and develop individual aggregation strategies for feature extractors and classifiers, respectively. Finally, extensive experimental results on five benchmark datasets under three different heterogeneous data scenarios demonstrate the effectiveness of our proposed FedGPA.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 82-92"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847949","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}