On the Additive Compositionality of Task Vectors in Vision–Language Models

AbstractIn-context learning (ICL) in large language models (LLMs) has been shown to operate through task vectors—the representation that summarizes the mapping induced by in-context demonstrations and can be composed by simple arithmetic operations. While this phenomenon is well studied in LLMs, its extension to vision-language models (VLMs) remains underexplored. In this work, we systematically examine the additive compositionality of in-context task vectors in VLMs, extracted from text-side hidden representations. Specifically, we construct compositional visual reasoning tasks with clearly defined subtasks and extract task vectors from few-shot demonstrations. Empirical experiments show that the vector for a complex task can be approximated by adding the vectors of its constituent subtasks. Beyond this, we analyze token-level contextual embeddings and show that additive composition arises because complex-task representations emerge as the superposition of atomic subtask components, preserving semantic structure within the model’s activation space.