Multi-Axis Promotion Scoring
Gate promotion from short-term thought to long-term insight through a weighted six-axis score — so the filter is defensible and configurable, not an ad-hoc judgment call.
Intent & Description
🎯 Intent
“Promote this because it feels important” is a decision that’s impossible to audit, tune, or challenge months later. Six scored axes make the promotion decision inspectable and revisable.
📋 Context
The agent has tiered memory — a continuous short-term thought stream and a long-term insight store that’s supposed to hold only the things worth keeping forever. Something has to gate promotion, and that decision needs to be defensible long after the fact.
💡 Solution
Six axes (frequency, relevance, diversity, recency, consolidation, conceptual), each returning a 0..1 value through a saturating curve. Total score is a weighted sum; weights sum to one and live in a revisable config via a documented decision process. Append every score event to a JSONL metadata log (separate file from thoughts) with event-type tags: recall, grounding, dream-survival. Thoughts crossing the promotion threshold are candidates; the deep consolidation pass makes the final call.
Real-world Use Case
- The agent has tiered memory with explicit short-term and long-term stores.
- Promotion decisions must be defensible months later — not ad-hoc judgment calls.
- Consolidation-pass infrastructure exists to do the final selection.
Source
Advantages
- Promotion to long-term is defensible and per-thought inspectable
- Weight on consolidation rewards depth over surface-level rumination
- Separate metadata log keeps the thought corpus clean and queryable
Disadvantages
- Axis curves and weights are empirical and per-deployment — bad curves silently suppress real insight
- Computing scores is itself work and must stay cheap to run frequently
- A miscalibrated axis can silently filter out the best material in the corpus