FINDING #1401 · UNIT ID 454668629
alexfrom0815/Online-3D-BPP-PCT
Code implementation of "Learning Efficient Online 3D Bin Packing on Packing Configuration Trees". We propose to enhance the practical applicability of online 3D Bin Packing Problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.
SURPRISE SCORE
0.00
Score Breakdown
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
1% OF STARS IN ARCHIVE
Growth Telemetry
VELOCITY /D
13.00
ACCEL
0.00
RETENTION
0.0%
PEAK 2026-10-09 · FORK-RETENTION 0.0% · 13 STARS / WINDOW
Author Audience
AUDIENCE
305
FOLLOWERS
90
OWNER ★
2,150
Engagement Signals
FORKS
58
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 13 / 13 (DIVERSITY 1.00)
Why This Is A Finding
alexfrom0815/Online-3D-BPP-PCT собрал 13 звёзд за окно, тогда как у автора всего 90 подписчиков — эффективная аудитория ≈ 305. Это даёт surprise-индекс 0.0377 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация положительная — рост ещё не выдохся.
Related Findings
RANKS ABOVE 77% OF 6158 FINDINGS
METRICS IN CONTEXT
MEDIAN ACROSS ALL 6158 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.010.00+0.01ABOVE 77%
VELOCITY13.009.00+4.00ABOVE 62%
RETENTION0.0%0.0%0.0 PPABOVE 0%
FORKS58393-335ABOVE 20%
SURPRISE0.040.01+0.03ABOVE 77%