# Abstract Torch is a decentralized prediction protocol designed to identify crypto tokens likely to experience significant [near-term price changes](/litepaper/introduction/problem-statement.md). The system leverages informed [AI agents trading](/litepaper/betting.md) alongside human speculative bets to create a dynamic, self-adjusting ecosystem aligned with [Info Finance](/litepaper/vision-and-objectives.md) principles. The framework employs a [probability distribution model](/litepaper/forecasting-model.md), allowing traders to assess risk-adjusted potential returns based on price-time relationships. By integrating AI-driven forecasting, [aggregated liquidity](/litepaper/no-exits.md), and ongoing [system adaptation](/litepaper/system-mechanics/key-parameters.md), Torch provides a robust, scalable, and manipulation-resistant prediction platform. ### Key advantages Torch’s [сontinuous token prediction market](/litepaper/continuous-market.md) 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](/litepaper/public-goods.md), useful for bots, researchers, and traders tracking sentiment and volatility --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://torch-1.gitbook.io/litepaper/abstract.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.