{"ID":2838804,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17792","arxiv_id":"2511.17792","title":"Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?","abstract":"While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark that enables comprehensive evaluation of video world models' semantic reasoning, spatial estimation, and planning capabilities. Target-Bench provides 450 robot-collected scenarios spanning 47 semantic categories, with SLAM-based trajectories serving as motion tendency references. Our benchmark reconstructs motion from generated videos with a metric scale recovery mechanism, enabling the evaluation of planning performance with five complementary metrics that focus on target-approaching capability and directional consistency. Our evaluation result shows that the best off-the-shelf model achieves only a 0.341 overall score, revealing a significant gap between realistic visual generation and semantic reasoning in current video world models. Furthermore, we demonstrate that fine-tuning process on a relatively small real-world robot dataset can significantly improve task-level planning performance.","short_abstract":"While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark that enables comprehensive evaluation of video world models' semantic reasoning, spatial estimation, and plannin...","url_abs":"https://arxiv.org/abs/2511.17792","url_pdf":"https://arxiv.org/pdf/2511.17792v2","authors":"[\"Dingrui Wang\",\"Zhihao Liang\",\"Hongyuan Ye\",\"Zhexiao Sun\",\"Zhaowei Lu\",\"Yuchen Zhang\",\"Yuyu Zhao\",\"Yuan Gao\",\"Marvin Seegert\",\"Finn Schäfer\",\"Haotong Qin\",\"Wei Li\",\"Luigi Palmieri\",\"Felix Jahncke\",\"Mattia Piccinini\",\"Johannes Betz\"]","published":"2025-11-21T21:36:02Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\"]","methods":"[]","has_code":false}