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- **Event:** [AI Engineer World's Fair Hackathon 2026](https://cerebralvalley.ai/e/aiewf-hackathon-2026)
- **When:** Jun 27 at 9:00 AM – Jun 28 at 5:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Aarush Gupta](https://cerebralvalley.ai/u/bxptr), [Ronok Tanvir](https://cerebralvalley.ai/u/ronoktanvir), [Louis Mascari-Dumont](https://cerebralvalley.ai/u/louismd)
- **GitHub:** https://github.com/bxptr/superprediction.git
- **Demo video:** https://www.youtube.com/watch?v=lVMWb_adHKQ
- **Gallery:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/28

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.

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Markdown version of https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/28. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt