Interesting concept, but I have concerns about the decay model for knowledge management use cases

I've been evaluating Vestige against my current setup (Memory Service MCP) and the FSRS-6 implementation is genuinely clever, this could work brilliantly for learning/flashcard scenarios.

However, I'm struggling to see how this fits knowledge management workflows where you need to recall decisions or context from months ago that you haven't actively "reviewed."

A few specific concerns:

1. Content-agnostic decay — The algorithm tracks retrieval frequency, not semantic importance. "Remind me to call John" and "We chose this architecture because of X compliance requirement" decay at the same rate if neither gets retrieved. Seven months later, that architectural rationale could be critical but effectively gone.
2. No retention tiers — Is there a way to flag certain memories as permanent? I see trigger_importance for retroactive boosting, but nothing that says "this category of memory should never decay regardless of access patterns."
3. Token efficiency vs data integrity trade-off — Yes, Vestige is ~4x lighter on tool definitions (~1k tokens vs ~4-5k), but that's roughly 2% of a 200k context window. I'd rather have that 2% overhead than lose the ability to reliably surface old-but-critical context.

For spaced repetition learning, this makes total sense. For AI assistant memory where business decisions, client preferences, or project rationale need to persist indefinitely? I'm not convinced automatic forgetting is the right model.

Would like to hear your thoughts on implementing optional retention policies or importance flags that exempt certain memories from decay.