# The Montparnasse Algorithm for RNA Design

arXiv: https://arxiv.org/abs/2606.07562 · HTML: https://mlsanity.com/paper/2606.07562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 5 of 5)

- [Designing RNA Secondary Structures is Hard](https://mlsanity.com/paper/1710.11513/) (2017)
- [Generalized Nested Rollout Policy Adaptation](https://mlsanity.com/paper/2003.10024/) (2020)
- [Monte Carlo Inverse Folding](https://mlsanity.com/paper/2005.09961/) (2020)
- [Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems](https://mlsanity.com/paper/2401.10431/) (2024)
- [Generalized Nested Rollout Policy Adaptation with Limited Repetitions](https://mlsanity.com/paper/2401.10420/) (2024)

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

- [L0 Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression](https://mlsanity.com/paper/2610.13574/) (2026)

## Abstract (arXiv)

RNA design consists of discovering a nucleotide sequence that optimizes predefined criteria, such as secondary structure. It is useful for synthetic biology, medicine, and nanotechnology. We propose Montparnasse, a Monte Carlo search framework based on Generalized Nested Rollout Policy Adaptation, augmented with a problem-specific prior, slow and long adaptation at level 1, and a lexicographic multicriteria evaluation. Montparnasse solves all 100 puzzles of the Eterna100 V1 benchmark consistently faster than DesiRNA, the previous state of the art, across all time limits, reaching full coverage more than three times faster overall. On messenger RNA secondary structure optimization for hemoglobin alpha, it identifies sequences with more paired bases than the MFE-optimal solution of LinearDesign.