Workshops

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.

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 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.

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.

Large language models (LLMs) are increasingly used to simulate human-like personas for applications in research, education, healthcare, and interactive AI systems. While such persona modeling creates opportunities for interdisciplinary innovation, it raises challenges around authenticity, consistency, bias, and ethical deployment. This workshop brings together perspectives from AI, psychology, cognitive science, and human–computer interaction to advance robust methods, standardized evaluation frameworks, and responsible practices for persona modeling in LLMs. Through invited talks, panels, posters, and discussions, the event will chart a roadmap for interdisciplinary collaboration and future research in this emerging area.

This workshop aims to advance the field of video understanding by fostering discussions around holistic and generalist video foundation models. Building upon the Holistic Video Understanding (HVU) initiative and dataset introduced in 2019, we have successfully organized eight HVU workshops and tutorials at top-tier venues such as CVPR and ICCV, uniting researchers, practitioners, and students from around the world. These efforts have played a central role in moving the community beyond narrow action recognition tasks toward multi-faceted, semantic, and generalist video understanding.With the emergence of large-scale foundation models and video large language models (Video-LLMs), the landscape of video understanding is rapidly evolving. These models enable unified reasoning across spatial, temporal, and multimodal dimensions, yet introduce new challenges in scalability, efficiency, interpretability, and responsible deployment.The HVU Workshop 2025 will provide a platform to explore these frontiers, discussing topics such as multimodal representation learning, long-context reasoning, evaluation of general-purpose video systems, efficient adaptation and scaling laws, and the ethical and societal implications of video AI. Our goal is to bring together a diverse and inclusive community to define the next chapter of holistic, generalist, and responsible video understanding.

Agents have experienced significant growth in recent years, largely due to the rapid technological advancements of Large Language Models (LLMs). Although these agents benefit from LLMs' advanced generation proficiency, they still suffer from catastrophic forgetting and a limited context window size compared to the agents' needs in terms of contextual information. Knowledge Graphs (KGs) are a powerful paradigm for structuring and managing connected pieces of information while unlocking deeper insights than traditional methods. Their value is immense for tasks that require context, integration, and reasoning. However, this power comes at the cost of significant upfront and ongoing investment in construction, curation, and specialized expertise. The first version of this workshop aims at analyzing and discussing emerging and novel practices, ongoing research and validated or deployed innovative solutions that showcase the growing synergy between LLMs agents and KGs.

Large Language Models (LLMs) have transformed NLP research and applications, yet they are still predominantly trained on high-resource, globally dominant languages. This imbalance leads to poor performance and limited applicability for low-resource languages, which are rich in tone, morphology, and cultural meaning. As a result, current AI systems risk reinforcing linguistic inequality, cultural erasure, and lack of accessibility in critical domains like education and healthcare.This workshop aims to reframe language technology by centering low-resource languages, cultures, and epistemologies in the age of LLMs. We seek to bring together researchers, linguists, developers, healthcare professionals, and technologists to share insights and develop strategies for building inclusive, culturally grounded, and linguistically robust language models. The workshop emphasizes collaboration across disciplines and regions to ensure both technical advancement and social relevance.Key areas of focus include developing LLM architectures tailored to low-resource linguistic features, ethical and community-centered dataset collection, and multilingual benchmarks designed specifically for underrepresented languages. We also highlight the importance of healthcare and medical machine translation to support equitable access to information and improve public health outcomes. Ultimately, this workshop aims to advance responsible AI innovation that empowers low-resource language communities and shapes a more inclusive future for global language technologies.