Foundation models (FMs), with their emergent and reasoning abilities, are reshaping the future of scientific research and broader human society. However, as their intelligence approaches or surpasses that of humans, concerns arise regarding their responsible use in real-world applications, such as reliability, safety, transparency, and ethics. The workshop on reliable and responsible FMs delves into the urgent need to ensure that such models align with human values. The significance of this topic cannot be overstated, as the real-world implications of these models impact everything from daily information access to critical decision-making in fields like medicine and finance, especially for embodied FMs that directly interact with the physical world. Stakeholders, including developers, practitioners, and policymakers, care deeply about this because the reliable and responsible design, deployment, and oversight of these models dictate not only the success of AI solutions but also the preservation of societal norms, order, equity, and fairness. Some of the fundamental questions that this workshop aims to address are:* Diagnosis: How can we identify and characterize unreliable and irresponsible behaviors in FMs? Topics include prompt sensitivity, lack of self-consistency, and hallucinations in generation.* Evaluation: How should we assess the harmful capabilities of FMs and quantify their societal impact? * Sources: How can we pinpoint and understand the known or emerging sources of FM unreliability? This involves examining training data, optimization objectives, and architectural design.* Generalization: How can responsible and reliable properties be effectively adapted to increasingly advanced FMs, particularly as they incorporate new features such as more modalities or long CoT? * Governance: What principles or guidelines should inform the next generation of FMs to ensure they are reliable and responsible? How can real-time monitoring of these FMs be enabled?* Guarantee: Can we establish theoretical frameworks for reliably and responsibly provable FMs?* Practice: How to leverage domain-specific knowledge to guide FMs towards improved reliability and responsibility across diverse areas, such as drug discovery, education, or clinical health?