3 papers
cs.LG2026
Dyad: Extending Large Language Models with Native Typed Decision-Making
Yundaichuan Zhan, Weishi Wang, Wenbiao Liu +5
We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dyad, an architecture that augme…
cs.CV2025
When are radiology reports useful for training medical image classifiers?
Herman Bergström, Zhongqi Yue, Fredrik D. Johansson
Medical images used to train machine learning models are often accompanied by radiology reports containing rich expert annotations. However, relying on these reports as inputs for…
cs.LG2025
Expanding the Action Space of LLMs to Reason Beyond Language
Zhongqi Yue, Weishi Wang, Yundaichuan Zhan +3
Large Language Models (LLMs) are powerful reasoners in natural language, but their actions are typically confined to outputting vocabulary tokens. As a result, interactions with ex…