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Superprediction is a continual learning system over prediction markets. We perform rollouts at inference time with Gemma E4B, which has search capabilities by Exa. Say at time t we generate a probability of a given market state. By t+1, we have a true probability of t. If we're incorrect, we generate a retroactive trace with Gemma 26B for a plausible CoT (with live search tool calls) and perform on-policy self-distillation into our E4B model via a method reminiscent of ECHO, where we RL on the binary outcome at time t and SFT on the tool calls, to efficiently encode optimal prediction strategies. We use Gemini 3.5 Pro to adjust our CoT so it does not leak information at t+1 and could have been theoretically generated at t by a strong predictor. With this, we hillclimb on prediction tasks from 0% to 12.7% after SFT on around 100k market timesteps and continual learning for ~1 hour on polled Polymarket data. Deployed in the real world, this system would continually learn real-world nuances and dynamics to maintain a market edge in an increasingly instituionalized exchange.