# Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets

arXiv: https://arxiv.org/abs/2607.20441 · HTML: https://mlsanity.com/paper/2607.20441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 12 May 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 3 of 3)

- [Approaching Human-Level Forecasting with Language Models](https://mlsanity.com/paper/2402.18563/) (2024)
- [AIA Forecaster: Technical Report](https://mlsanity.com/paper/2511.07678/) (2025)
- [An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia wars](https://mlsanity.com/paper/2601.06132/) (2026)

## Built on by (most cited first, 1 of 1)

- [The Corroboration Illusion: When More News Makes LLM Forecasts Less True](https://mlsanity.com/paper/2609.22246/) (2026)

## Abstract (arXiv)

Every information ecosystem produces beliefs that shape strategic decisions. Both human analysts and AI systems inherit the blind spots of their information sources. We show that LLMs, combined with prediction markets, function as a calibrated instrument for measuring how far ecosystem-induced beliefs deviate from an external reference: LLMs extract the beliefs a text corpus implies, and prediction market price trajectories, anchored at resolution by realised outcomes, provide the calibration reference against which to quantify the deviation. We isolate the bias contribution of specific text through ablation: varying information context while holding the model fixed, with a contaminated model that knows actual outcomes as control. Applied to 111 Ukraine-related prediction markets, comprising approximately 93,000 predictions across four models, we find that English news context systematically biases territorial predictions, wrong 64 to 72 percent of the time when it pushes predictions toward territorial capture. A contaminated model that knows actual outcomes shows the same error rate, indicating that the bias originates primarily in the text. Supplementing with Ukrainian military-analytical sources reduces the bias for all clean models, while absolute-error gains are partial and model-dependent. We show that the distortion originates primarily in the sources, not the models. Consistent across four architectures, it will persist in any system that processes them and propagate into downstream decisions.