How Evolution Works

The loop

          +-- (session N occurs) --+
          |                        v
          |                   signals recorded
          |                        |
          |                        v
          |              enough sessions/time?
          |              /                  \
          |            no                  yes
          |            |                    |
          |            |               generate challenger
          |            v                    |
          +---- keep champion            A/B test starts
                                             |
                                             v
                                      P(challenger > champion) >= 0.95?
                                        /                          \
                                      yes                           no
                                       |                             |
                                       v                             v
                                 promote challenger              hold / keep running

Sessions → fitness

Each session's signals are collapsed into one fitness score in [0, 1]:

• Implicit signals (tests passed, user accepted, no /clear) each contribute with weight 1.
• Explicit signals (evolve good / evolve bad) each contribute with weight 5.
• The weighted mean is clamped to [0, 1].

Fitness → promotion

Scores are binarized at 0.5 (win / loss) and fed into a Beta-binomial posterior per arm:

• Champion ~ Beta(1 + champ_wins, 1 + champ_losses)
• Challenger ~ Beta(1 + chall_wins, 1 + chall_losses)

A 10,000-sample Monte Carlo estimates P(challenger > champion). When that exceeds 0.95 and both arms have at least 20 sessions, the challenger is promoted.

Why 0.95 and not a straight A/B frequentist test? Bayesian posteriors let us peek — we can check after every session without inflating false positives.