4 accepted papers
Understanding \emph{modality interaction} in multimodal large language models (MLLMs) remains a central challenge for reliable and interpretable deployment. We introduce Partial Information Decomposition (PID) as a unified, decision-level framework that separates \emph{unique}, \emph{redundant}, and…
Molecule generation is key to drug discovery and materials science, enabling the design of novel compounds with specific properties. Large language models (LLMs) can learn to perform a wide range of tasks from just a few examples. However, generating valid molecules using representations like SMILES
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), a crucial task for numerous applications such as recommendation systems and drug repurposing. The success of knowledge graph embedding (KGE) models provokes the question about the explainability…
Text-to-image generation has transformed content creation, yet precise visual text rendering remains challenging for generative models due to blurred glyphs, semantic inconsistencies, and limited style controllability. Current methods typically employ pre-rendered glyph images as conditional inputs,…