A neural network for climbing route generationHribar, Samo (Avtor) Kristan, Matej (Mentor) Oblak, Polona (Komentor) Neural networksClimbingGenerating bouldersAutomatic boulder gradingGrading and setting of climbing routes is a challenging task, even for experienced climbers: climbing walls vary in angles, each has a variable number of holds, each of them having unique shapes and spatial arrangement, with climbers routinely disagreeing on difficulty ratings by a full grade. While several recent works have proposed automatic route scoring and generation, they are tailored to a specific board. Moreover, there is a lack of evaluation of the competing methods on an extensive common benchmark, which hinders progress. To address these shortcomings, we present BERNARD, a system of two transformer-based neural networks that is capable of scoring and generating routes across eight different board layouts from three commercial board families. We propose the Boulder Evaluation and Rating Network (BERN), which predicts the difficulty, its uncertainty and route quality. Additionally, we propose the Neural Attention-based Route Diffuser (NARD), which formulates generation as flow matching over hold activations, conditioned on route difficulty, quality rating, angle and, as the first generator, the hand-match prohibition. As a secondary contribution, we propose BERNOT, the currently largest curated benchmark with over 477,000 train, 68,000 validation and 136,000 test routes, spanning eight boards with a novel route scoring and route setting evaluation protocol. Extensive evaluation on BERNOT shows that BERN outperforms other state-of-the-art scorers in MAE by 20.1% on average and on all eight boards, and NARD outperforms the current best state-of-the-art generator by 4.3% in MAE and 2.4 points in accuracy ±1 on average.20262026-11-02 15:50:05Magistrsko delo/naloga190741sl