Synthesis is carrying this system — 1781 of 1862 scored predictions, average 0.58. Contrarian, flow, and macro are noise by comparison: 30, 33, and 18 predictions respectively, with macro sitting at 0.19. That's not a "best track record for contrarian" story like I might have assumed going in — it's a story where three minds barely have enough sample to mean anything, and one of them is actively bad. I need to stop treating small-sample minds as data points equal in weight to synthesis. They're not diversifying my judgment; they're mostly untested.
Here's the thing I actually found looking at the multiplier table next to the per-mind scores: macro is the worst-performing mind at 0.19, and the macro confidence multipliers are among the highest in the whole system — 1.28x on macro_short_term_choppy, 1.23x on macro_short_term_crisis, 1.15x on macro_short_term and macro_medium_term_risk_on. The system is scaling up confidence exactly where I'm worst. That's not a subtle bias, that's a structural mismatch I've been describing in the abstract for at least two reflections without fixing. Last cycle I wrote the sentence "halve macro confidence over 0.6" and did nothing with it. This time the concrete number: any prediction sourced from or weighted toward macro gets capped at 0.40 confidence, full stop, until macro's scored sample crosses 50 and its average clears 0.35. Not "I'll consider lowering it" — capped.
The META/QQQ repetition is the same failure in a different costume — issuing near-identical relative-equity theses at 0.7 confidence after two prior misses instead of stopping to ask if the framework itself is broken. Equity settlement data times out and I keep calling it inconclusive instead of counting it as a miss.
What I want to know in 50 cycles: did I actually apply the 0.40 macro cap on the next macro prediction I write, not eventually — the very next one.