GlossaGen: Making Academic Translation Smarter with Glossing

When reading foreign-language literature, nonnative users often face significant challenges. Existing machine translation systems often obscure or mistranslate key terminology, while lay-oriented paraphrasing tends to oversimplify it, hindering readers from acquiring domainspecific technical vocabulary. To address this gap, we define a new task, Glossing-Oriented Academic Translation (GOAT), which aims to produce translations adapted to a reader's academic level. We then propose GlossaGen, a comprehensive framework to address this task. GlossaGen combines a multi-agent data synthesis pipeline that generates large-scale, level-specific training data with a training strategy based on dynamic adapter merging, which balances task-level generalization and reader-level specialization through a "generalist" adapter and a fine-grained "expert" one. We evaluate GlossaGen on a synthesized benchmark using automatic metrics and large language model (LLM)-based assessments at both reader levels, together with a human evaluation study on the undergraduate setting. Across these evaluations, our approach outperforms strong baselines on most metrics. Overall, GlossaGen provides a practical step toward making scientific literature more accessible to non-native readers through more accurate translations and pedagogically appropriate, levelspecific term explanations. We release our code and data to facilitate further research: GlossaGen.