Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models-even when the guidance is correct and problem-relevant. We show that this instability arises from a previously underexplored gap between strategy usage-whether a reasoning strategy appears in successful solutions-and strategy executability-whether the strategy remains effective when instantiated as guidance for a target model. Through a controlled analysis of paired human-written and model-generated solutions, we identify a systematic dissociation between usage and executability: human- and model-derived strategies differ in structured, domain-dependent ways, leading to complementary strengths and consistent source-dependent reversals under guidance. Building on this diagnosis, we propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability by selectively retrieving and combining strategies using empirical, multi-route, source-aware signals. Across multiple mathematical reasoning benchmarks, SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance, improving accuracy by up to $+13$ points on AIME25 and $+5$ points on Apex for compact reasoning models. Code and benchmark are publicly available at: https://github.com/lwd17/strategy-execute-pipeline.
StraGo: Harnessing Strategic Guidance for Prompt Optimization
Institute of Software, CAS（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft（微软公司）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation
Northeastern University（东北大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Kunming University of Science and Technology（昆明理工大学）；NiuTrans Research（牛翻译研究院）
XR-CareerAssist: An Immersive Platform for Personalised Career Guidance Leveraging Extended Reality and Multimodal AI
Institute of Communications and Computer Systems (ICCS)（通信与计算机系统研究所）；DASKALOS-APPS；National Technical University of Athens（雅典国家技术大学）；University of Exeter（埃克塞特大学）；CVCOSMOS Ltd（CVCOSMOS有限公司）
Leveraging Unlabeled Data from Unknown Sources via Dual-Path Guidance for Deepfake Face Detection
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；Visual Intelligence +X International Cooperation Joint Laboratory of MOE（教育部视觉智能+X国际合作联合实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Domain Adaptive SAR Wake Detection: Leveraging Similarity Filtering and Memory Guidance
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center（青岛海洋科学与技术中心区域海洋学与数值模拟实验室）；School of Computer Science and Information Technology, The University of Nottingham（诺丁汉大学计算机科学与信息学院）；State Key Laboratory of Physical Oceanography, Department of Marine Technology, Ocean University of China（中国海洋大学物理海洋学国家重点实验室，海洋技术系）
JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
ServiceNow AI Research（ServiceNow AI研究机构）；Chandar Research Lab（Chandar研究实验室）；Mila -- Quebec AI Institute（魁北克人工智能研究院）；Dalhousie University（达尔豪斯大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
Reward and Guidance through Rubrics: Promoting Exploration to Improve Multi-Domain Reasoning
Key Laboratory of Network Data Science（网络数据科学实验室）；Technology, Institute of Computing Technology, Chinese Academy of Sciences（技术，计算技术研究所，中国科学院）；State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Merced（加州大学梅尔塞德斯分校）
