Decentralized Aggregation of LLM Predictions via Wagering Mechanisms
It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance. In decentralized settings, aggregation weights need to be determined without access to models' private information and should remain robust to strategic reporting. We propose a family of advantage-aligned wagering mechanisms for LLM aggregation (WALLA), in which each model reports a prediction and a learned wager, and predictions are aggregated using wagers as weights. WALLA introduces a leave-one-out baseline into the net payout function, yielding three desirable properties: (1) dominant-strategy incentive com...
| Authors | Yuhong Luo, David M. Pennock, Xintong Wang |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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