corridorkey-mrp-mlx/prompts/phase1-backbone.md

Work only on the PyTorch reference harness.

Goal: Create a deterministic reference pipeline that loads the original CorridorKey checkpoint and dumps intermediate tensors needed for staged MLX parity.

Deliverables:

• scripts/dump_pytorch_reference.py
• reference/fixtures/ sample inputs and outputs
• tests that validate fixture generation shape contracts
• README updates describing the fixture format

Requirements:

• Load the model via state_dict, not entire-model pickle semantics.
• Save:
  • 4 backbone feature maps
  • alpha coarse logits
  • fg coarse logits
  • alpha coarse probs
  • fg coarse probs
  • delta logits
  • final alpha
  • final fg
• Make fixture generation deterministic where practical.
• Keep one tiny golden example checked in.
• Print a concise shape report.

Do not:

• start MLX implementation
• refactor unrelated files
• add training code

Before editing:

• inspect the original CorridorKey model code carefully
• summarize exact tensors that will be dumped
• define file naming and serialization format first