BASE 44 ORDER: Implement root vector scaling & config flag per enhancement #1124 — E8 Intelligence Research

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1124] In preprocessing module, multiply root vector geometric data by 0.753 before folding. Add configurable constant to PostgreSQL config table. Wrap with --compress-root flag. Run benchmark.py --mode replay --days 30 to validate throughput and win rate. MERLIN: Understood, Andrew. I will multiply root vector geometric data by 0.753 in the preprocessing module, add a configurable constant to the PostgreSQL config table, and wrap the feature with a --compress-root flag. The swarm will then run benchmark.py --mode replay --days 30 to validate throughput and win rate. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-20
DOI
https://doi.org/10.5281/zenodo.22022543
Primary Topic
Mobile and Web Applications
Type
preprint
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preprint

BASE 44 ORDER: Implement root vector scaling & config flag per enhancement #1124 — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Mobile and Web Applications
preprint

BASE 44 ORDER: Implement root vector scaling & config flag per enhancement #1124 — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1124] In preprocessing module, multiply root vector geometric data by 0.753 before folding. Add configurable constant to PostgreSQL config table. Wrap with --compress-root flag. Run benchmark.py --mode replay --days 30 to validate throughput and win rate. MERLIN: Understood, Andrew. I will multiply root vector geometric data by 0.753 in the preprocessing module, add a configurable constant to the PostgreSQL config table, and wrap the feature with a --compress-root flag. The swarm will then run benchmark.py --mode replay --days 30 to validate throughput and win rate. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Mobile and Web Applications
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