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Fix decay baseline so new memories start Active instead of Fading

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Changed zero-access usage_factor from 0.5 to 1.0 in calculate_decay_score().
Previously, a freshly stored solution (importance=0.7, weight=1.2) scored
0.42 (Fading); now scores 0.84 (Active) as intended.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Cal Corum 2026-02-14 19:20:07 -06:00
parent 807c6978e5
commit 08b74ec6d6
1 changed files with 1 additions and 1 deletions
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skills/cognitive-memory/client.py
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@ -361,7 +361,7 @@ def calculate_decay_score(
decay_score = importance * e^(-lambda * days) * log2(access_count + 1) * type_weight
"""
time_factor = math.exp(-DECAY_LAMBDA * days_since_access)
usage_factor = math.log2(access_count + 1) if access_count > 0 else 0.5
usage_factor = math.log2(access_count + 1) if access_count > 0 else 1.0
return importance * time_factor * usage_factor * type_weight