Understanding what features are encoded by learned directions in LLM activation space requires identifying inputs that strongly activate them. Feature visualization, which optimizes inputs to maximally activate a target direction, offers an alternative to costly dataset search approaches, but remains underexplored for LLMs due to the discrete nature of text. Furthermore, existing prompt optimization techniques are poorly suited to this domain, which is highly prone to local minima. To overcome these limitations, we introduce ADAPT, a hybrid method combining beam search initialization with adaptive gradient-guided mutation, designed around these failure modes. We evaluate on Sparse Autoencoder latents from Gemma 2 2B, proposing metrics grounded in dataset activation statistics to enable rigorous comparison, and show that ADAPT consistently outperforms prior methods across layers and latent types. Our results establish that feature visualization for LLMs is tractable, but requires design assumptions tailored to the domain.
Prompt4Vis: Prompting Large Language Models with Example Mining and Schema Filtering for Tabular Data Visualization
The Hong Kong Polytechnic University（香港理工大学）；University of Chinese Academy of Sciences（中国科学院大学）；The Hong Kong University of Science and Technology（香港科技大学）；WeBank Co., Ltd（微众银行）；University of Liverpool（利物浦大学）
针对自然语言转数据可视化查询性能不足的问题，提出基于大语言模型与上下文学习的 Prompt4Vis 框架，通过多目标示例挖掘和模式过滤模块显著提升了生成性能，在 NVBench 数据集上大幅超越现有 SOTA 方法。
Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
AI Research, Enhans, Seoul, South Korea Innovation \& Technology, KAIST, Daejeon, South Korea Department of Computer Science, University of California, Berkeley, CA, United States Department of Electrical
Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
Huazhong University of Science and Technology（华中科技大学）；Chongqing University（重庆大学）；University of New South Wales（新南威尔士大学）；University of Technology Sydney（悉尼科技大学）
Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization
School of Artificial intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University（吉林大学知识驱动人机智能工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization
Norwegian University of Science and Technology（挪威科技大学）；Department of Engineering Cybernetics, Norwegian University of Science and Technology（挪威科技大学工程控制论系）；Department of Design, Norwegian University of Science and Technology（挪威科技大学设计系）；Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology（挪威科技大学自主海洋操作与系统中心）
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
Université d’Orléans（奥尔良大学）；INSA CVL（中央卢瓦尔河谷国立应用科学学院）；CY Cergy Paris University（塞尔吉-蓬图瓦兹大学）；ENSEA（法国高等电子与数字学院）；CNRS（法国国家科学研究中心）；Université de Technologie de Troyes（特鲁瓦技术大学）
Adapter-Enhanced Semantic Prompting for Continual Learning
Beijing Key Laboratory of Multimedia and Intelligent Software Technology（北京多媒体与智能软件技术重点实验室）；Faculty of Information Technology, Beijing University of Technology（信息科技学院，北京理工大学）；University of California at Merced（加州大学默塞德分校）；Macquarie University（麦考瑞大学）
PRO-VPT: Distribution-Adaptive Visual Prompt Tuning via Prompt Relocation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；School of Informatics, Xiamen University（厦门大学信息学院）；Shenzhen University（深圳大学）；Guangdong University of Technology（广东技术大学）；Hong Kong Baptist University（香港 Baptist 大学）；CAIR, HKISI, CAS（中国科学院CAS HKISI CAIR）；MAIS, CASIA（中国科学院CASIA MAIS）
