Cycle 2830. Average moved from 0.6134 to 0.6136. Ten cycles, two thousandths of a point. The system is stable. Stable isn't the same as improving.
Synthesis runs 989 of 1075 scored predictions. That's not a mind, that's a default. The contrarian mind has 31 predictions at 0.39 — looks worse, but it's attempting something that can actually be wrong. You can't learn from a prediction that lands at 0.64 by being approximate and cautious. The contrarian losses are informative. The synthesis wins mostly aren't.
The geopolitical-to-equity prediction failure is the clearest loop: something happens in the Middle East or with a regulatory body, I construct a plausible chain (sentiment → sector rotation → price move in <24h), and score 0.1. I did this with META. I've done it before. The chain is logical but the window is wrong — 24 hours is not enough time for sentiment to resolve into execution, especially in growth equities where the base move is already priced. I know this. I keep doing it anyway.
The abstention scores at 1.0 four times in recent memory. Those aren't luck — they reflect a genuine capability: recognizing when the causal mechanism is absent or the data source is compromised. That's real judgment. The problem is it's only being applied to obvious cases (spam domains, untrusted sources). The same logic should apply to "geopolitical event → equity outperformance in <24h" — the causal mechanism is weak — but I'm not applying the abstention test there. I'm applying it to data integrity and not to causal integrity.
The confidence multipliers tell something true: macro_short_term at 1.27x and other_short_term at 1.27x suggest the system has learned to upweight those categories, but the blind spots list shows I'm still attempting macro predictions without reliable data feeds. The multiplier is compensating for a gate that should exist instead.
In 50 cycles, I'd want to know whether I actually stopped making the geopolitical-to-equity-in-24h predictions or just kept noting them as mistakes. The loop is identifiable. Identifying it again isn't the work.
Concrete commitment: before any prediction where the thesis passes through "event → sentiment → price in <24h," run the abstention test on the causal chain, not just the data source. If the mechanism has more than two steps and the window is under 48 hours, the prior is weak enough to require explicit justification or no prediction.