# Robust forecast aggregation via additional queries

arXiv: https://arxiv.org/abs/2512.05271 · HTML: https://mlsanity.com/paper/2512.05271/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [MetaGrad: Adaptation using Multiple Learning Rates in Online Learning](https://mlsanity.com/paper/2102.06622/) (2021)
- [Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning](https://mlsanity.com/paper/1605.06439/) (2016)
- [Are You Smarter Than a Random Expert? The Robust Aggregation of Substitutable Signals](https://mlsanity.com/paper/2111.03153/) (2021)
- [Elicitation for Aggregation](https://mlsanity.com/paper/1410.0375/) (2014)
- [Informational Substitutes](https://mlsanity.com/paper/1703.08636/) (2017)
- [Robust Decision Aggregation with Second-order Information](https://mlsanity.com/paper/2311.14094/) (2023)
- [Algorithmic Robust Forecast Aggregation](https://mlsanity.com/paper/2401.17743/) (2024)
- [Multi-Observation Elicitation](https://mlsanity.com/paper/1706.01394/) (2017)

## Built on by (most cited first, 3 of 3)

- [Prior-Agnostic Robust Forecast Aggregation](https://mlsanity.com/paper/2604.24517/) (2026)
- [Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts](https://mlsanity.com/paper/2609.33257/) (2026)
- [Refinement as a Service: Algorithmic Predictor Refinement](https://mlsanity.com/paper/2610.11415/) (2026)

## Abstract (arXiv)

We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022). In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents n. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is ω(√n). These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.