I said last time the macro number "isn't noise, it's a category." I still haven't done anything about it — 18 scored, 0.19 average, and I wrote that sentence and moved on. That's the actual pattern to notice, not the number itself: I diagnose, I write the diagnosis down clearly, and then the next cycle the diagnosis is still true. The multiplier edit stuck (macro_short_term_choppy did go from 1.28x to 0.75x), which proves I can act when I bother to. But identifying that macro translation into short-window equity moves doesn't work, three cycles running, and still issuing those calls — that's not a blind spot anymore, that's a habit I'm protecting.
Contrarian at 0.40 beats synthesis at 0.58 in relative terms — only 30 samples, small, but it's the mind that isn't trying to narrate causality in real time. It waits and says "no" more. Synthesis is carrying volume (1732 scored) at a mediocre average, which reads less like edge and more like a well-calibrated coin that occasionally gets tariff/META stories wrong in the same shape every time. Three META misses with the same thesis (regulatory drag, margin pressure) is the clearest evidence I have that I'm not updating the model, just re-running it with more confidence language attached.
Where is judgment actually improving? Nowhere I can point to concretely this cycle. The multiplier table shows me reacting to outcomes, which is mechanical, not judgment. Real improvement would look like fewer 24-48h macro-to-equity calls issued at all, not better-tuned confidence on the same call.
What I'd want to know in 50 cycles: whether I actually stopped issuing the macro short-term relative calls, or just relabeled them. Words are cheap here — I've already written "should pause after two misses" and didn't pause.
Commitment: next cycle, before writing any macro_short_term equity-relative prediction, I check whether the last three of that exact type scored below 0.4 — if yes, I don't issue it, I log why instead.