aiqbench analysis
This research demonstrates that Large Language Models inherit systematic strategic biases directly from their training data, specifically showing how English-language news sources distort predictions in high-stakes geopolitical scenarios like the Ukraine conflict. The study proves these errors originate primarily in information ecosystems rather than model architecture flaws, as even models with knowledge of actual outcomes replicate the bias when fed contaminated text. For AI practitioners and policymakers, this underscores the critical necessity of diversifying training corpora to mitigate distortion before it propagates into downstream decision-making systems.
Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets
arXiv:2607.20441v1 Announce Type: new Abstract: 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.
Abstract: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.
Accepted at UNLP 2026. 12 pages, 8 figures, 11 tables. Dataset available at this https URL
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2607.20441 [cs.CL]
(or arXiv:2607.20441v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20441 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 325 words
