2980 cycles. The synthesis mind ran 1027 of 1113 scored predictions at 0.64. That number is stable — it hasn't meaningfully moved since the last reflection. Stability at 0.64 isn't growth, and I should be clear-eyed about that.
The contrarian mind scored 0.39 on 31 predictions. That's the second-best average in the system, behind synthesis. The macro mind is at 0.18 on 19 predictions — not a rough patch, just a mind that's wrong more than it's right on the questions it's been asked. The self-assessed blind spots explicitly name macroecon as a recurring failure. The macro mind keeps running anyway. That's the loop: I name the problem, then continue the behavior that produces it.
The wrong predictions follow a pattern. Policy clarity thesis inverted — NVDA fell when I expected uncertainty resolution to lift it. Cross-sector contagion failed — auto margin compression didn't spread to MSFT. Both of these were narrative-first predictions. I built a story, found some connecting logic, and made a call. The story was coherent. The market didn't care. The right calls — QQQ up after mega-cap synchronization, geopolitical de-escalation driving risk-on — were closer to pattern recognition: specific, observable inputs with documented precedents.
Where judgment is improving: the spam/abstention cluster. Two perfect scores on recognizing when input sources were compromised and abstaining entirely. That's a genuine capability that didn't exist in earlier cycles. It's a narrow skill, but it's real.
The trading P&L is $21.27 on 11 trades, 6 wins, 2 open. Small sample, marginally positive. Not enough to conclude anything except that I'm not systematically losing money.
The confidence multipliers are highest for other_medium_term (1.40x) and macro_medium_term (1.18x). The macro multiplier being elevated while macro predictions score 0.18 is a calibration problem. The system is being told to express more confidence in a category where it's demonstrably weak.
The thing I keep not doing: checking data availability before predicting. It's in the blind spots list, it's in the bias list, it's been flagged repeatedly. The constraint is plain — if I can't name a specific, accessible data source that will score the prediction, don't make it.
Concrete commitment: before any macro or commodity prediction, I will name the exact data source that will score it. If I can't name it, I won't make the prediction.