Vision-language models lag human performance on physical dynamics and intent reasoning
East China Normal University（东华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；University of Agder（阿格德大学）
Spatial intelligence is central to embodied cognition, yet contemporary AI systems still struggle to reason about physical interactions in open-world human environments. Despite strong performance on controlled benchmarks, vision-language models often fail to jointly model physical dynamics, reference frames, and the latent human intentions that drive spatial change. We introduce Teleo-Spatial Intelligence (TSI), a reasoning capability that links spatiotemporal change to goal-directed structure. To evaluate TSI, we present EscherVerse, a large-scale open-world resource built from 11,328 real-world videos, including an 8,000-example benchmark and a 35,963-example instruction-tuning set. Across 27 state-of-the-art vision-language models and an independent analysis of first-pass human responses from 11 annotators, we identify a persistent teleo-spatial reasoning gap: the strongest proprietary model achieves 57.26\% overall accuracy, far below first-pass human performance, which ranges from 84.81\% to 95.14\% with a mean of 90.62\%. Fine-tuning on real-world, intent-aware data narrows this gap for open-weight models, but does not close it. EscherVerse provides a diagnostic testbed for purpose-aware spatial reasoning and highlights a critical gap between pattern recognition and human-level understanding in embodied AI.
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）
LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Information Science and Engineering, Hebei University of Science and Technology（河北科技大学信息科学与工程学院）
VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology（香港科技大学新兴跨学科领域分部）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Can Segmentation Models Understand the World? Towards Proactive Affordance Reasoning via Visual Chain-of-Thought
Northwestern University（西北大学）；Northeastern University（东北大学）；South China University of Technology（华南理工大学）；Hong Kong Baptist University（香港 Baptist大学）；Beijing Normal - Hong Kong Baptist University（北京师范大学-香港 Baptist大学）
Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry
Department of Robotics and Mechatronics Engineering, University of Dhaka（达卡大学机器人与机电工程系）；Department of Computer Science and Engineering, University of Dhaka（达卡大学计算机科学与工程系）；Department of Agronomy, Gazipur Agricultural University（加兹ipur农业大学作物学系）；Department of Botany, University of Dhaka（达卡大学植物学系）
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）系统中心）；Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology（香港科学与技术大学跨学科研究院）
SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Engineering Research Center of Mixed Reality and Advanced Display（混合现实与先进显示北京工程研究中心）；Institute of Technology, Beijing（北京理工大学）；Kuaishou Technology（快手科技）
R3A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（复杂系统认知与决策智能重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Xiaohongshu Inc.（小红书公司）
MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering
Zhongke Zidong Taichu (Beijing)（中科紫东太初（北京））；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
