{"type":"ScholarlyArticle","title":"World4V2X: A Consistency-driven World Model for Robust V2X Cooperative Perception","authors":[{"name":"Rui Wang","slug":"rui-wang","given":"Rui","family":"Wang","affiliation":""},{"name":"Shuai Wang","slug":"shuai-wang","given":"Shuai","family":"Wang","affiliation":""},{"name":"Xiangyi Qin","slug":"xiangyi-qin","given":"Xiangyi","family":"Qin","affiliation":""},{"name":"Ze Yu","slug":"ze-yu","given":"Ze","family":"Yu","affiliation":""},{"name":"Xiaojun Tan","slug":"xiaojun-tan","given":"Xiaojun","family":"Tan","affiliation":""}],"abstract":"Cooperative perception allows agents to extend perceptual capabilities through inter-agent communication. However, most existing methods still adopt a frame-wise paradigm, which limits the exploitation of spatio-temporal consistency in dynamic scenes. Therefore, when observations are partially occluded or degraded, these methods are unable to leverage historical context for compensation, leading to unstable perception and reduced detection accuracy. To address these challenges, we propose World4V2X, the first world model framework tailored for V2X cooperative perception. The proposed method first introduces a spatial observability modeling module that defines spatial consistency boundaries to distinguish reliable regions from uncertain ones, thereby enabling spatial consistency modeling over multi-agent heterogeneous observations. Building upon this, we construct a consistency-guided world modeling module. Specifically, a temporal consistency evolution mechanism leverages features from historical and current frames to model the state evolution of the scene, capturing environmental dynamics and producing temporal consistency beliefs. Meanwhile, a consistency-guided deterministic reconstruction mechanism exploits spatial boundaries and temporal beliefs to perform diffusion-based refinement for robust cooperative perception. Extensive experiments on the OPV2V and V2XSet datasets demonstrate that World4V2X achieves state-of-the-art perception performance across diverse V2X scenarios.","doi":"10.24963/ijcai.2026/195","url":"https://ijcai.org/proceedings/2026/195","pdf":"https://ijcai.org/proceedings/2026/0195.pdf","markdown":"https://ijcai.org/proceedings/2026/195.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/195","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":195,"pages":{"first":1749,"last":1757},"keywords":["Computer Vision: Recognition (object detection, categorization)","Robotics: Perception","Multidisciplinary Topics and Applications: Transportation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}