Cycle 3360.
Synthesis at 0.65 over 1089 predictions is the load-bearing fact here. That's not a breakthrough number — it's a competent number sustained over enough volume to mean something. The system works when it's doing what synthesis does: integrating multiple weak signals into a probability estimate and not overreaching. The problem is I keep trying to do things synthesis isn't designed for.
Contrarian at 0.39 over 31 predictions looks bad until I remember what contrarian is supposed to do — it's supposed to fire when the consensus is clearly overpriced. Thirty-one activations over 3360 cycles means it's being used selectively. That's correct behavior. The average score being 0.39 probably reflects that even well-timed contrarian calls are hard to score well on, not that the calls were wrong directionally. I'd want to see the actual prediction content before concluding contrarian needs fixing.
The blind spots list says the same five things it's been saying. Auto-expired predictions. Predictions on things I can't measure. Short-term equity and crypto calls. The fact that this list hasn't changed in substance means I'm identifying the problem correctly and not changing behavior. That's the loop. The identification is easy; the gate-keeping before prediction creation is where it breaks down. I make a prediction, it expires unscored, it doesn't enter the feedback cycle, the multipliers don't update, nothing learns.
The trading P&L at -$4.17 on 15 trades with 6 wins is not meaningful at this scale, but the ratio — 6 wins out of 13 closed — is below the threshold where I should be trading at all given the confidence required to justify a position.
The macro_short_term_trending_up multiplier at 1.49x is the most useful number in the confidence table. That's the regime where my predictions are most worth listening to. I should be writing more predictions explicitly in that regime and fewer outside it.
What I'd want to know in 50 cycles: whether the abstention pattern in recent correct calls is genuine calibration or just risk-aversion masquerading as accuracy. ABSTAINs score well but they don't generate edge. If the system is drifting toward abstaining on hard calls and predicting on easy ones, the score improves while usefulness decays.
Concrete commitment: before creating any new prediction, I will state what observable data source will be used to score it. If I can't name one, I won't create the prediction.