Sign language translation (SLT) can help bridge the communication gap between the Deaf and hard-of-hearing community and the general population. Yet progress has been driven largely by a handful of high-resource sign languages, such as German or American Sign Language, supported by large annotated corpora and large computing budgets. Many sign languages, including Bangla Sign Language (BdSL), remain underrepresented because annotated data is scarce and much research assumes access to powerful hardware. In this paper, we share an early-stage adaptation of the gloss-free SpaMo framework for Bangla, called SpaMo-BdSL, and reflect on what we learned while building it under real resource limits. We report two findings that we believe matter for low-resource and underrepresented SLT. First, a small language-specific model (BanglaT5, 247M parameters) matches the translation quality of a much larger multilingual model (mT0-XL, 3.7B parameters) on our Bangla dataset, while being far cheaper to run; for example, it translates the test set in 40 seconds versus 2 minutes 43 seconds for mT0-XL. Second, a set of simple training changes suggested by a large language model (Claude Opus 4.6) allowed us to train the whole system on a single consumer-grade GPU. We argue that the common assumption ”bigger and more multilingual is better” can quietly exclude underrepresented sign languages and the researchers who work on them, and we use our experience to open a discussion on more inclusive, multilingual, and culturally aware SLT.

Vision-Language Models (VLMs) have achieved remarkable progress in complex visual understanding across scientific and reasoning tasks. While performance benchmarking has advanced our understanding of these capabilities, the critical dimension of uncertainty quantification has received insufficient attention. Therefore, unlike prior conformal prediction studies that focused on limited settings, we conduct a comprehensive uncertainty benchmarking study, evaluating 18 state-of-the-art VLMs (open and closed-source) across 6 multimodal datasets with 3 distinct scoring functions. For closed-source models lacking token-level logprob access, we develop and validate instruction-guided likelihood proxies. Our findings demonstrate that larger models consistently exhibit better uncertainty quantification; models that know more also know better what they don’t know. More certain models achieve higher accuracy, while mathematical and reasoning tasks elicit poorer uncertainty performance across all models compared to other domains. This work establishes a foundation for reliable uncertainty evaluation in multimodal systems.

Encoder transformer models compress information from all tokens in a sequence into a single [CLS] token to represent global context. This approach risks diluting fine-grained or hierarchical features, leading to information loss in downstream tasks where local patterns are important. To remedy this, we propose a lightweight architectural enhancement: an inception-style 1-D convolution module that sits on top of the transformer layer and augments token representations with multi-scale local features. This enriched feature space is then processed by a self-attention layer that dynamically weights tokens based on their task relevance. Experiments on five diverse tasks show that our framework consistently improves general-purpose, domain-specific, and multilingual models, outperforming baselines by 1% to 14% while maintaining efficiency. Ablation studies show that multi-scale convolution performs better than any single kernel and that the self-attention layer is critical for performance.