Decentralized Aggregation of LLM Predictions via Wagering Mechanisms

We propose WALLA, a decentralized mechanism for aggregating probabilistic predictions from multiple LLMs using wagering mechanisms. Each model reports a prediction and a learned wager reflecting its expected score advantage; predictions are then aggregated using wagers as weights. Our mechanism introduces a leave-one-out baseline that yields three key properties: dominant-strategy incentive compatibility under general belief structures, a best-response wager proportional to expected score advantage, and decoupling of prediction and wager optimization. We instantiate two mechanism variants trading off normality and no-arbitrage, both with bounded worst-case deficit independent of the number of participants. Experiments on QA benchmarks and a forecasting benchmark show that WALLA matches centralized baselines while simultaneously achieving advantage-weighted aggregation, uncertainty-awareness, fully decentralized learning, and incentive-compatibility guarantees.