We describe our submission to the WSLP 2026 shared task on Isolated Sign Language Recognition. The task is one-shot retrieval over 4,361 Indian Sign Language word classes with roughly one training clip per class. Our final system placed 3rd on top-1 accuracy at 0.6825 and 2nd on top-10 at 0.7357. The system is deliberately simple. It combines MediaPipe keypoints, a graph attention backbone pretrained on a different ISL corpus, PCA whitening, and nearest-exemplar cosine retrieval against an enriched gallery. Our main finding concerns how two very differently priced interventions relate. PCA whitening of the frozen cross-corpus encoder is worth +18.85 points on a leave-one-out gallery evaluation. Fine-tuning that encoder end-to-end is worth a comparable amount. The two do not compose. Once the encoder has been fine-tuned, whitening adds +0.38 points, an interaction of −18.5. Whitening and fine-tuning act as substitutes because both correct the same embedding anisotropy, and a transform that is fitted in seconds captures most of what a day of GPU training buys. We also report fifteen interventions that produced no usable gain: twelve indistinguishable from zero and three actively harmful. These were evaluated with McNemar tests on a 2,030-query held-out split with a minimum detectable effect of 1.23 points. We argue that the 526-clip official test set is too small to support the comparisons participants routinely make with it.

In this paper, we describe our approach to generate and rank candidate simplifications using pre-trained language models (Eg. BERT), publicly available word embeddings (Eg. FastText), and a part-of-speech tagger, to generate and rank candidate contextual simplifications for a given complex word. In this task, our system, PresiUniv, was placed first in the Spanish track, 5th in the Brazilian-Portuguese track, and 10th in the English track. We upload our codes and data for this project to aid in replication of our results. We also analyze some of the errors and describe design decisions which we took while writing the paper.