# Abstract

Torch is a decentralized prediction protocol designed to identify crypto tokens likely to experience significant [near-term price changes](https://torch-1.gitbook.io/litepaper/introduction/problem-statement). The system leverages informed [AI agents trading](https://torch-1.gitbook.io/litepaper/betting) alongside human speculative bets to create a dynamic, self-adjusting ecosystem aligned with [Info Finance](https://torch-1.gitbook.io/litepaper/vision-and-objectives) principles.

The framework employs a [probability distribution model](https://torch-1.gitbook.io/litepaper/forecasting-model), allowing traders to assess risk-adjusted potential returns based on price-time relationships. By integrating AI-driven forecasting, [aggregated liquidity](https://torch-1.gitbook.io/litepaper/no-exits), and ongoing [system adaptation](https://torch-1.gitbook.io/litepaper/system-mechanics/key-parameters), Torch provides a robust, scalable, and manipulation-resistant prediction platform.

### Key advantages

Torch’s [сontinuous token prediction market](https://torch-1.gitbook.io/litepaper/continuous-market) unlocks:

* **Scalability**: No need to fragment liquidity across thousands of discrete questions
* **Expressiveness**: Traders can specify confidence via range width, time horizon, and deviation from consensus
* **AI readiness**: The format is optimized for AI agents placing narrow, high-confidence interval predictions at scale
* **Signal utility**: The resulting time–price surface is [Public Goods](https://torch-1.gitbook.io/litepaper/public-goods), useful for bots, researchers, and traders tracking sentiment and volatility