Decentralized aggregation of LLM predictions via betting

The growing adoption of large language models (LLMs) in enterprise environments has opened opportunities to combine predictions from multiple expert systems, each with access to private data or specific domains. However, when these models operate in a decentralized manner, the challenge arises of aggregating their predictions without compromising each agent's privacy and ensuring that economic incentives do not distort reports. A novel approach uses betting mechanisms aligned with each model's actual advantage, where each participant contributes not only a prediction but also a bet that weights their contribution. This scheme allows, through a payment function that incorporates an exclusion baseline, desirable properties to be achieved: dominant strategy incentive compatibility for predictions, proportionality between the optimal bet and the expected advantage, and decentralized optimization of betting policies without requiring perfect predictions. Furthermore, it is possible to design variants that balance the normality of the payment distribution with the absence of arbitrage, while keeping the mechanism's maximum deficit bounded. In practice, this approach aligns with the vision of companies seeking to integrate artificial intelligence in a robust and scalable manner. For example, at Q2BSTUDIO we offer AI services for businesses that enable the implementation of customized prediction aggregation systems, leveraging specialized AI agents across different domains. These systems can benefit from cloud infrastructures such as AWS or Azure, as reflected in our cloud services AWS and Azure, which ensure scalability and low latency. Likewise, the betting and weighting logic can be integrated within business intelligence platforms, such as Power BI, to visualize the confidence of aggregated predictions. Cybersecurity also plays a key role in protecting each model's proprietary data during the aggregation process. From custom application development to automation solutions, at Q2BSTUDIO we address these challenges with a comprehensive approach, combining custom software, artificial intelligence, and cybersecurity so that organizations can orchestrate their language models in a decentralized, efficient, and reliable manner.