Imagine a team of specialized AI assistants for coding, each a whiz at a particular task like bug fixing or code generation. Now, imagine merging their expertise into a single powerhouse. That’s the idea behind MergeRepair, a research project exploring how to combine the strengths of individual AI “adapters” to create a supercharged tool for automated program repair (APR). Think of it like assembling a team of code-fixing Avengers. Each adapter is trained on a specific task, like adding new features (Development), fixing bugs (Bug Fixes), or improving existing code (Improvement). MergeRepair aims to blend these specialized skills, reducing the need to train separate AI models for each task and potentially boosting performance overall. The study looks at three main techniques for merging these adapters and two different scenarios: one where all adapters contribute equally, and a “continual learning” scenario where new adapters are added sequentially, mimicking the evolution of a real-world coding project. Why is this important? Training AI models for code-related tasks takes time and resources. If MergeRepair works, it could lead to faster, more efficient AI tools that can be easily adapted to new coding challenges. While the research is focused on Python code, the insights could apply more broadly. This has the potential to unlock new levels of productivity for developers and potentially reshape how software is built and maintained.