# Strategic Candidacy in Generative AI Arenas

arXiv: https://arxiv.org/abs/2603.26891 · HTML: https://mlsanity.com/paper/2603.26891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF](https://mlsanity.com/paper/2312.08358/) (2023)
- [The Leaderboard Illusion](https://mlsanity.com/paper/2504.20879/) (2025)
- [Search Arena: Analyzing Search-Augmented LLMs](https://mlsanity.com/paper/2506.05334/) (2025)
- [Copilot Arena: A Platform for Code LLM Evaluation in the Wild](https://mlsanity.com/paper/2502.09328/) (2025)
- [Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?](https://mlsanity.com/paper/2505.23749/) (2025)
- [Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards](https://mlsanity.com/paper/2501.07493/) (2025)
- [You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism](https://mlsanity.com/paper/2110.14802/) (2021)
- [Improving Your Model Ranking on Chatbot Arena by Vote Rigging](https://mlsanity.com/paper/2501.17858/) (2025)
- [Clone-Robust AI Alignment](https://mlsanity.com/paper/2501.09254/) (2025)
- [The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review](https://mlsanity.com/paper/2408.13430/) (2024)
- [Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings](https://mlsanity.com/paper/2508.11847/) (2025)
- [Incentive-Theoretic Bayesian Inference for Collaborative Science](https://mlsanity.com/paper/2307.03748/) (2023)
- [Instance-Adaptive Hypothesis Tests with Heterogeneous Agents](https://mlsanity.com/paper/2510.21178/) (2025)
- [Leaderboard Incentives: Model Rankings under Strategic Post-Training](https://mlsanity.com/paper/2603.08371/) (2026)

## Built on by (most cited first, 1 of 1)

- [Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation](https://mlsanity.com/paper/2605.30916/) (2026)

## Abstract (arXiv)

AI arenas, which rank generative models from pairwise preferences of users, are a popular method for measuring the relative performance of models in the course of their organic use. Because rankings are computed from noisy preferences, there is a concern that model producers can exploit this randomness by submitting many models (e.g., multiple variants of essentially the same model) and thereby artificially improve the rank of their top models. This can lead to degradations in the quality, and therefore the usefulness, of the ranking. In this paper, we begin by establishing, both theoretically and in simulations calibrated to data from the platform Arena (formerly LMArena, Chatbot Arena), conditions under which producers can benefit from submitting clones when their goal is to be ranked highly. We then propose a new mechanism for ranking models from pairwise comparisons, called You-Rank-We-Rank (YRWR). It requires that producers submit rankings over their own models and uses these rankings to correct statistical estimates of model quality. We prove that this mechanism is approximately clone-robust, in the sense that a producer cannot improve their rank much by doing anything other than submitting each of their unique models exactly once. Moreover, to the extent that model producers are able to correctly rank their own models, YRWR improves overall ranking accuracy. In further simulations, we show that indeed the mechanism is approximately clone-robust and quantify improvements to ranking accuracy, even under producer misranking.