arXiv:2610.09118v1 Announce Type: new
Abstract: Topology reasoning jointly detects 3D lanes and traffic elements from multi-view images and infers their structural connectivity. Current methods model...
By Mihai Bogdan Deaconu, Laura Dio\c{s}an
arXiv:2510. 04100v2 Announce Type: replace-cross Abstract: Topological mapping offers a compact and robust representation for navigation, but progress in the field is hindered by the lack of standardized evaluation metrics, datasets, and protocols.
By Jiaming Wang, Jizhuo Chen, Diwen Liu, Harold Soh
arXiv:2609.16378v1 Announce Type: cross
Abstract: Digital twins provide a scalable and cost-effective complement to real-world testing for validating autonomous-driving and advanced driver-assistance...
By Ghazal Farhani, Taufiq Rahman
The paper introduces TopoBench-180, a human‑verified benchmark of 180 structural diagrams with canonical graph annotations, and TopoAgent, a perception‑to‑reasoning framework that extracts graph topology from diagrams using large vision‑language models. TopoAgent combines grounded perception, global structural priors, node inventory construction, local‑to‑global relation reasoning, and consistency enforcement to progressively build the target graph. Experiments demonstrate that TopoAgent surpasses strong baselines, particularly in edge extraction, thereby advancing multimodal structured understanding for diagram‑to‑graph tasks.
By Bangwei Guo, Xujiang Zhao, Yanchi Liu, Wei Cheng, Shengyu Chen, Dongyue Li, Masaharu Morimoto, Takayuki Kuroda, Dimitris Metaxas, Haifeng Chen
MindTopo is a benchmark that tests foundation models on topological reasoning, covering five cognitive properties—continuity, separation, order, enclosure, and knots—across two cognitive levels: reasoning and planning. It contains 11,030 instances from 13 procedurally generated task types, and evaluates 14 multimodal large language models, including agent configurations with image and video generation. Results show that models perform better on reasoning than planning, and even the best model lags far behind human performance, with fine‑tuning and reinforcement learning improving reasoning more than planning.
By Yunfei Ge, Anbang Liu, Qineng Wang, Johnalbert Garnica, Jianwen Lyu, Zihan Wang, Reuben Tan, Jianfeng Gao, Ruohan Zhang, Yining Hong, Jiajun Wu, Manling Li
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang