Japanese Sign Language (JSL) is a low-resource sign language that has received limited attention in the AI research community, primarily due to the lack of large-scale, publicly available parallel corpora. In this work, we introduce J-Shuwa, a large-scale JSL-Japanese parallel corpus constructed from YouTube videos with hard-coded subtitles and closed captions. The corpus contains 197K parallel JSL-Japanese sentence pairs, totaling approximately 300 hours of video, making it the largest publicly available JSL dataset to date. We conduct sign language translation (SLT) experiments by training models on J-Shuwa and evaluating them on the JSL Dialogue Corpus under both zero-shot and fine-tuned settings. Our results demonstrate that J-Shuwa is effective for training SLT models. Beyond SLT, we believe that J-Shuwa can also serve as a valuable resource for future JSL research across a wide range of tasks. The dataset and code are publicly available at: https://github.com/SpaJune/J-Shuwa.

Vision-language models (VLMs) can comply with harmful requests delivered through images, even when their LLM backbones would refuse the same content in text. While prior work characterizes these jailbreaks empirically or at the representation level, how visual inputs perturb safety pathways at the neuron level remains uncharted. We close this gap with a causal, neuron-level analysis of safety mechanisms in 10 VLMs. We propose a two-stage detection pipeline with iterative ablation that accounts for self-repair, and introduce two modality-isolated benchmarks, ViSafe-Detect and ViSafe-Eval, which decouple visual and textual safety signals.

Our analysis reveals: (i) Text safety in VLMs is localizable: ~88 neurons (<0.01%) whose targeted ablation substantially reduces refusal. (ii) Text safety neurons constitute the dominant refusal pathway: ablating them is the only intervention that consistently and substantially reduces refusal across all models. (iii) Visual safety is high-dimensional and diffuse at the single-neuron level: text safety concentrates in ~5 subspace directions while visual safety requires ≥50. This gap holds across architectures, explaining why current alignment has not closed the visual safety gap. Project page is at: https://jiaxuan-li.github.io/vlm-safety-neuron/.

Warning: this paper may include examples of harmful content.

Sign language understanding remains a significant challenge, particularly for low-resource sign languages with limited annotated data. Motivated by the success of large-scale pretraining in deep learning, we propose Multi-Stream Masked Autoencoder (MS-MAE) — a simple yet effective framework for learning sign language representations from skeleton-based video data. We pretrained a model with MS-MAE on the YouTube-ASL dataset, and then adapted it to multiple downstream tasks across different sign languages. Experimental results show that MS-MAE achieves competitive or superior performance on a range of isolated sign language recognition benchmarks and gloss-free sign language translation tasks across several sign languages. These findings highlight the potential of leveraging large-scale, high-resource sign language data to boost performance in low-resource sign language scenarios. Additionally, visualization of the model’s attention maps reveals its ability to cluster adjacent pose sequences within a sentence, some of which align with individual signs, offering insights into the mechanisms underlying successful transfer learning.