Compound AI Systems Workshop

Developers are increasingly constructing Compound AI Systems, i.e. systems that use multiple model calls and/or external components, to tackle the most challenging AI tasks. These systems can often outperform models alone, but the best way to design these systems and the components that go into them (e.g., retrievers and tools) is still an open problem. This workshop will gather researchers working in this emerging area to investigate questions such as:

• Designs for compound AI systems
• Efficient execution and training of compound AI systems
• Programming models for AI systems
• Optimizing AI systems end-to-end
• Planning and execution (decomposing tasks)
• Evaluation methods and metrics
• Privacy and security Improvements to core components like retrievers
• Methods for tool use
• Debugging and MLOps
• Scaling laws of compound AI systems
• Applications