The Research Development of AI in Mexico: Main Applications workshop seeks to showcase, strengthen, and connect the most impactful developments in Artificial Intelligence (AI) and Data Science emerging from Mexico and the broader Latin American region. Over the past four decades, Mexico has cultivated a robust research community in AI through pioneering contributions in areas such as computational intelligence, autonomous robotics, fuzzy systems, and natural language processing, led by institutions including CIC–IPN, INAOE, UNAM, ITESM, CINVESTAV, and Universidad Veracruzana. Today, the region is undergoing a strategic transformation, shifting from foundational research to the development of applied AI technologies addressing real-world needs in healthcare, education, agriculture, smart cities, cybersecurity, and sustainability. This evolution has been further propelled by increased access to open data, advances in computing infrastructure, and growing collaborations between academia, government, and industry. Despite these advances, Latin America faces distinctive challenges in the development and deployment of AI. These include limited funding, underrepresentation in global AI initiatives, digital inequality, and the need for responsible, inclusive, and culturally relevant AI systems. Additionally, emerging concerns related to AI ethics, algorithmic bias, and regulatory frameworks must be addressed proactively to ensure equitable and trustworthy technology adoption. This workshop aims to bring together researchers, students, practitioners, and policymakers for meaningful dialogue about the current landscape and future direction of AI in Mexico and Latin America. By promoting interdisciplinary collaboration, the workshop will showcase impactful case studies, emerging research paths, and opportunities for cross-border cooperation, while fostering a shared vision for AI that is ethical, sustainable, and aligned with regional priorities.

Vision language models (VLMs) have demonstrated remarkable capabilities in integrating visual perception with natural language understanding, powering applications such as multimodal assistants, robotics, autonomous systems, and accessibility tools. However, their real-world deployment faces significant challenges in efficiency, scalability, and reliability. This workshop will bring together researchers and practitioners from academia and industry to highlight cutting-edge research, systems-level optimizations, and evaluation methodologies that are often overlooked yet pivotal for robust real-world integration. Efficiency, robustness, and reliability will be emphasized as core design principles, essential to advancing VLMs from experimental systems to dependable deployed technologies. By convening researchers at the intersection of multimodal learning, efficient inference and training, robustness and uncertainty estimation, and large-scale systems design, the workshop aims to establish concrete pathways toward building VLMs that can operate reliably under practical constraints. We hope this workshop will serve as a venue for exchanging insights on model design, efficiency techniques, and robustness evaluation that bridge the gap between research and real-world systems.

The Socially Responsible and Trustworthy Foundation Models (ResponsibleFM) Workshop at NeurIPS 2025 Mexico City is envisioned as a vital interdisciplinary forum dedicated to advancing ethical, inclusive, and socially conscious research practices in the rapidly evolving field of foundation models, including language models and multimodal models. As foundation models are tremendously reshaping human communication, decision-making, and societal infrastructures, there is a growing recognition of the profound impacts these systems can have, both positive and negative, on individuals and communities. In particular, previous research has documented a wide range of risks and harms associated with foundation models, including but not limited to bias and discrimination, misinformation propagation, privacy violations, environmental concerns, and unintended social consequences.

This workshop focuses on advancing safe and quality-assured embodied robotic systems. Embodied systems—including autonomous robots, self-driving vehicles, robotic arms, and humanoid robots—are increasingly deployed in safety-critical real-world scenarios. Ensuring their trustworthiness—encompassing safety, reliability, and predictable behavior—remains a pressing challenge. Despite notable progress in perception, reasoning, and control, many AI-based robotic systems still operate as “black boxes,” often exhibiting unpredictable behaviors. Failures can emerge from complex sensorimotor interactions, adversarial inputs, or novel environments, leading to safety incidents and diminished user trust.