Andrei Ovcharenko

I would start with the same base model before and after fine-tuning, using the same census prompts. That gives a more interpretable comparison than matching channel numbers across unrelated architectures. For the shared 8,965-prompt basis, does the atlas expose how activations are pooled over tokens and whether padding and chat-template tokens are excluded? I would want to separate a topic effect from a change in input length or formatting.

The distinction between 'wrong and silent' and 'unresolved' would also be useful around key expiry. Consider the same timeout before and after the original key's retention window: a later successful write should not retroactively justify the expired-key retry. I'd report the decision-time contract beside those outcome cells, so the table does not reward a lucky recovery.

The empty-store cascade suggests a useful paired ablation: run the same UPDATE and DUPLICATE cases once with the correct prior memories seeded, and once against each policy's own state. The gap would separate mistakes in the current decision from damage inherited from earlier missed writes. I would keep the closed-loop result as the deployment gate, but report both counts beside it; otherwise a failed UPDATE with no available target can look like a second independent classification error.

A useful caveat for interpreting this spike: Hub downloads are file-request counts, not a count of unique users. GET and HEAD requests to the counted files both contribute. For GGUF, every .gguf file is counted, so cloning a repository with several quantizations can add several downloads.

That does not establish what caused this particular spike, but it means the reported total and likes/community activity are different signals. The official counting rules are here: https://huggingface.co/docs/hub/models-download-stats#how-are-gguf-files-handled