corridorkey-mrp-mlx/prompts/phase2-mlx-blocks.md

Work only on the MLX implementations of the custom non-backbone blocks.

Goal: Implement MLX versions of:

• MLP
• DecoderHead
• RefinerBlock
• CNNRefinerModule

Requirements:

• Use MLX idioms and explicit NHWC handling.
• Centralize tensor layout transforms in one utility module.
• Use pytorch-compatible GroupNorm behavior where needed for parity.
• Write parity tests that use saved PyTorch backbone features and saved coarse predictions.
• Report max abs error and mean abs error in test output or helper scripts.

Do not:

• port Hiera yet
• optimize prematurely
• spread layout conversions across many files

Definition of done:

• decoder parity test exists
• refiner parity test exists
• modules are wired into a partial model path for test usage