model stitching for understanding

**Model stitching for understanding** is the **technique that connects layers from different models with learned adapters to test representational compatibility** - it probes whether internal representations can substitute for each other functionally. **What Is Model stitching for understanding?** - **Definition**: A stitching layer maps activations from source model layer to target model layer input space. - **Compatibility Signal**: Successful stitched performance suggests aligned intermediate representations. - **Granularity**: Can test correspondence at specific layer depths or full-block boundaries. - **Interpretation**: Provides functional evidence beyond static similarity metrics alone. **Why Model stitching for understanding Matters** - **Functional Comparison**: Directly tests interchangeability of learned representations. - **Architecture Insight**: Reveals where different model families compute similar abstractions. - **Transfer Learning**: Helps identify layers with reusable features. - **Research Rigor**: Adds performance-based evidence to representational analysis. - **Complexity**: Adapter quality and training setup can confound interpretation if uncontrolled. **How It Is Used in Practice** - **Control Baselines**: Compare stitched models against random and identity adapter controls. - **Layer Sweep**: Evaluate multiple stitch points to map compatibility landscape. - **Task Diversity**: Test stitched performance across varied tasks before broad claims. Model stitching for understanding is **a functional method for testing internal representation interoperability** - model stitching for understanding is strongest when adapter effects are benchmarked against rigorous controls.