Towards Foundation Models For Consensus Rank Aggregation

# Towards Foundation Models for Consensus Rank Aggregation

**arXiv ID:** 2603.15218
**Authors:** Yijun Jin, Simon Klüttermann, Chiara Balestra, Emmanuel Müller
**Published:** 2026-03-16T12:55:54Z
**Abstract:**
Aggregating a consensus ranking from multiple input rankings is a fundamental problem with applications in recommendation systems, search engines, job recruitment, and elections. Despite decades of research in consensus ranking aggregation, minimizing the Kemeny distance remains computationally intractable. Specifically, determining an optimal aggregation of rankings with respect to the Kemeny distance is an NP-hard problem, limiting its practical application to relatively small-scale instances. We propose the Kemeny Transformer, a novel Transformer-based algorithm trained via reinforcement learning to efficiently approximate the Kemeny optimal ranking. Experimental results demonstrate that our model outperforms classical majority-heuristic and Markov-chain approaches, achieving substantially faster inference than integer linear programming solvers. Our approach thus offers a practical, scalable alternative for real-world ranking-aggregation tasks.

## Skill Description

This skill is generated from the arXiv paper: Towards Foundation Models for Consensus Rank Aggregation (2603.15218).

## How to Use

[To be filled in by the user or by future automation]

## References

- [arXiv:2603.15218](http://arxiv.org/abs/2603.15218v1)