About the Symposium

Our mission is to foster interdisciplinary collaboration to develop fully autonomous AI systems, addressing challenges like benchmark datasets, human-AI collaboration, robust tools and methods for validating AI outputs, and trustworthiness. By tackling these issues, we can unlock AI's transformative potential in research. In this symposium, themed Agentic AI for Science, we will explore these critical topics and welcome diverse perspectives. We will focus on integrating agentic AI systems to enhance scientific discovery while upholding rigorous standards. For AI to contribute effectively, it must generate novel hypotheses, comprehend their applications, quantify testing resources, and validate feasibility through well-designed experiments. This symposium serves as a vital forum for collaboration and knowledge-sharing aimed at redefining the landscape of scientific discovery. This symposium aims to address four main research thrusts to propel future research, including (non-exclusively):

Thrust 1. Design and development of agentic AI systems for scientific discovery. The emergence of agentic AI, powered by foundation models—particularly generative models—opens up unprecedented opportunities for scientific discovery. These systems can potentially revolutionize various aspects of the scientific process, including hypothesis generation, comprehension of complex scientific phenomena, quantification, and validation. Designing and developing effective agentic AI systems for scientific discovery is both exciting and non-trivial. Pioneering work in this field has already demonstrated the promise of leveraging scientific tools, agents, and knowledge graphs. Notable examples include ChemCrow, which showcases the potential of AI in chemistry; Crispr-GPT, which applies AI to genetic engineering; and SciAgents , which illustrates the power of multi-agent systems in scientific discovery. These groundbreaking studies highlight the transformative potential of agentic AI in accelerating scientific progress and opening new avenues for research. Key research topics in this thrust include (but not limited to):

• Developing scientific foundation models: Tailoring general foundation models specifically for various scientific fields to enhance relevance and accuracy.
• Effective scientific tool augmentation: Enhancing existing scientific tools and methodologies with agentic AI capabilities.
• Multi-agent decomposition design: Developing frameworks for scientific hypothesis generation using multiple specialized AI agents.
• Human-in-the-loop agentic systems: Improving reliability and interpretability of AI-driven scientific discoveries through strategic human intervention.

Thrust 2. Theoretical foundation for scientific agentic AI. Developing agentic scientific AI requires methods to quantify the predictions and performance of these systems, as well as to validate the scientific hypotheses they generate. A thorough investigation of agentic scientific AI systems also demands solid theoretical foundations and tools to ensure guarantees on their behavior. To analyze and evaluate such systems, we will incorporate theoretical tools in modeling, logical reasoning, model validation and diagnosis, interpretable AI, and other general methods that can provide guarantees on agentic systems. Key topics in this area include, but are not limited to, the following:

• Theoretical foundation: Statistical models and theories of agentic scientific AI, such as theoretical studies on in-context learning, multi-agent communications, game theory, physics-informed hard and soft optimization constraints, and neural operators.
• Logic reasoning: Inductive, deductive, and abductive reasoning; Bayesian reasoning and probabilistic programming; neural-symbolic approaches.
• Model quantification, validation, diagnosis: Theory-driven metrics for quantifying AI system performance; self-evaluation of LLMs; data valuation and data-centric AI; diagnostics for data, architecture, and training processes; creation of standardized benchmarks for evaluating the validity of scientific hypothesis generation; scientific facts and hallucination.
• Interpretable AI: Approaches for explaining agentic AI system behaviors; quantifying trust, safety, and transparency; mechanistic interpretability.

Thrust 3. Practical application of scientific agentic AI. Deploying agentic AI systems in practical scientific research across diverse domains presents numerous challenges, particularly due to the need for domain-specific adaptation such as the unique data formats and model constraints of each scientific field. Bias in training data poses a significant risk, especially in sensitive domains like medicine. Trustworthiness and explainability are essential for scientists to confidently integrate AI-generated hypotheses and solutions into their research. Furthermore, ethical considerations arise when AI systems potentially automate research decisions that may impact public health, policy, or environmental outcomes, underscoring the importance of responsible AI deployment in science.

• Domain-specific model adaptation: Adapting agentic AI models to handle domain-specific data formats, workflows, and tools across various scientific fields; transfer learning and data-efficient fine-tuning.
• Bias detection and mitigation: Identifying and mitigating bias in training data, model design and outputs; fairness-aware AI systems for sensitive domains like healthcare and social science.
• Robustness, trustworthiness and explainability: Methods for improving the transparency and explainability of agentic AI systems in scientific research; uncertainty interpretation and quantification.
• Ethical considerations and responsible use of agentic AI in sensitive research areas; development of AI governance models to ensure accountability and human oversight in automated scientific workflows.

Thrust 4. Open problems and challenges on scientific agentic AI. Despite the promising potential of agentic AI in scientific discovery, many open problems and challenges remain to be addressed. These may include:

• Automatic curation of domain-specific scientific domains and integration of the knowledge into agentic AI systems.
• Advanced mechanisms of multi-agent collaboration in scientific discovery, with considerations of their scalability and computational efficiency.
• Continual evolution and learning of agentic AI systems; Mechanisms for updating models and improving performance based on experimental results, new data and discoveries.
• Validation and reproducibility of results generated by agentic AI systems.