In the era of AI-driven transformations, foundation models (FMs) have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities,reshape the future of scientific research and the broader human society, but also introduce challenges intheir in-the-wild/real-world deployments. The 2nd Workshop on FMs in the Wild delves into the urgent need forthese models to be useful when deployed in our societies. The significance of this topic cannot be overstated,as the real-world implications of these models impact everything from daily information access to criticaldecision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeplyabout this because the successful integration of FMs into in-the-wild frameworks necessitates a careful consideration of many properties, including adaptivity, reliability, efficiency, and reasoning ability. Some of thefundamental questions that this workshop aims to address are:1. In-the-wild Adaptation: How can we leverage techniques such as Retrieval-Augmented Generation(RAG), In-context Learning (ICL), or Fine-tuning (FT) to adapt FMs for specific domains, such asdrug discovery, education, or clinical health?2. Reasoning and Planning: How can FMs be enhanced to tackle more complex in-the-wild tasks thatrequire multi-step reasoning or decision-making, such as multi-hop question answering, mathematicalproblem-solving, theorem proving, code generation, or robot planning scenarios?3. Reliability and Responsibility: How can FMs work reliably outside their training distribution?And how can we address issues like hallucination, fairness, ethics, safety and privacy within the society?4. Practical Limitations in Deployment: How can FMs tackle challenges in practical applications,such as system constraints, memory requirements, response time demands, data acquisition barriers,and computational costs for inference-time scaling and long-context input?In summary, our topics of interest include, but are not limited to:* Innovations in techniques for customizing models to individual user preferences, tasks, or domains* Advancements in the reasoning and planning abilities of FMs in complex real-world challenges* Theoretical and empirical investigations into the reliability and responsibility of various FMs* Strategies for overcoming practical limitations (e.g., memory, time, data) of FMs in broad applications* Methods for integrating multiple modalities (e.g., text, images, action) into a unified in-the-wild framework* Discussions on FM agents that perform intricate tasks through interaction with the environment* In-depth discussions exploring the in-the-wild deployments and applications of FMs* Benchmark methodologies for assessing FMs in real-world settings