# Beyond Forecasting: The Belief-to-Trade Layer in Prediction-Market Agents

arXiv: https://arxiv.org/abs/2607.03015 · HTML: https://mlsanity.com/paper/2607.03015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities](https://mlsanity.com/paper/2409.19839/) (2024)
- [FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction](https://mlsanity.com/paper/2508.11987/) (2025)
- [LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena](https://mlsanity.com/paper/2510.17638/) (2025)
- [PolyBench: Benchmarking LLM Forecasting and Trading Capabilities on Live Prediction Market Data](https://mlsanity.com/paper/2604.14199/) (2026)
- [Prediction Arena: Benchmarking AI Models on Real-World Prediction Markets](https://mlsanity.com/paper/2604.07355/) (2026)
- [Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs](https://mlsanity.com/paper/2604.18576/) (2026)

## Abstract (arXiv)

Forecasting future events has attracted growing attention as a testbed for general-purpose AI. A natural way to ground this evaluation is let the models trade in the prediction markets. Trading, however, requires more than forecasting. Moreover, recent benchmarks report a substantial gap between calibrated probability scores and the trading results. We propose Raven-Agent, to the best of our knowledge, the first autonomous trading agent for prediction markets. On a controlled replay over an archived decision set, our architecture achieves the only positive return and the only positive risk-adjusted return among all tested policies. We have released our code in https://github.com/Alchemist-X/predict-raven .