Vision-language models (VLMs) have shown a promising ability in image geolocation, but they still lack structured geographic reasoning and the capacity for autonomous self-evolution. Existing methods predominantly rely on implicit parametric memory, which often exploits outdated knowledge and generates hallucinated reasoning. Furthermore, current inference is a "one-off" process, lacking the feedback loops necessary for self-evolution based on reasoning outcomes. To address these issues, we propose GeoSkill, a training-free framework based on an evolving Skill-Graph. We first initialize the graph by refining human expert trajectories into atomic, natural-language skills. For execution, GeoSkill employs an inference model to perform direct reasoning guided by the current Skill-Graph. For continuous growth, an Autonomous Evolution mechanism leverages a larger model to conduct multiple reasoning rollouts on image-coordinate pairs sourced from web-scale data and verified real-world reasoning. By analyzing both successful and failed trajectories from these rollouts, the mechanism iteratively synthesizes and prunes skills, effectively expanding the Skill-Graph and correcting geographic biases without any parameter updates. Experiments demonstrate that GeoSkill achieves promising performance in both geolocation accuracy and reasoning faithfulness on GeoRC, while maintaining superior generalization across diverse external datasets. Furthermore, our autonomous evolution fosters the emergence of novel, verifiable skills, significantly enhancing the system's cognition of real-world geographic knowledge beyond isolated case studies.
Where on Earth? A Vision-Language Benchmark for Probing Model Geolocation Skills Across Scales
University of Central Florida（中央佛罗里达大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Columbia University（哥伦比亚大学）；Amazon Research（亚马逊研究）；University of California, Berkeley（加州大学伯克利分校）
SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
The Hong Kong University of Science and Technology（香港科学与技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Nanjing University（南京大学）；The University of Hong Kong（香港大学）
Skill-Conditioned Gated Self-Distillation for LLM Reasoning
Tsinghua University（清华大学）；Fudan University（复旦大学）；City University of Hong Kong（香港城市大学）；Huazhong University of Science and Technology（华中科技大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；The Chinese University of Hong Kong（香港中文大学）；University of Science and Technology of China（中国科学技术大学）；University of Chinese Academy of Sciences（中国科学院大学）；vivo AI Lab（vivo AI实验室）
Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；College of Software Engineering, Southeast University（东南大学软件学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Attention-Spectrum Regularization for Replay-Free Continual Multimodal LLMs
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Sun Yat-sen University（中山大学）；Nanyang Technological University（南洋理工大学）；Renmin University of China（中国人民大学）；Tsinghua University（清华大学）
Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai AI Laboratory（上海人工智能实验室）；Software Engineering Institute, East China Normal University（东华大学软件工程学院）
SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills
The Ohio State University（俄亥俄州立大学）；The University of Chicago（芝加哥大学）；University College London（伦敦大学学院）；University of Michigan（密歇根大学）；The Chinese University of Hong Kong（香港中文大学）；Case Western Reserve University（凯斯西储大学）；Amazon（亚马逊）
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents
Tongji University（同济大学）；The University of Sydney（悉尼大学）；University of Science and Technology of China（中国科学技术大学）；Nankai University（南开大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；City University of Hong Kong（香港城市大学）；University of Surrey（萨里大学）
