Visual recoding as a strategy for enabling divergent creativity in an LLM poetry generation task

Large language models (LLMs) have revolutionized technical tasks with clear and objective quality signals, such as code generation and mathematical proofs, but yield mixed results on creative, divergent reasoning tasks such as fiction and poetry. Poetry generation may serve as a benchmark for divergent creativity because it requires the ability to construct and stabilize meaning that transcends the well-learned lexical associations of its constituent words within a highly constrained form. Across diverse poetic traditions, visualization has been used to foster novel poetic insights. Here, we used two LLM families (Gemini and Qwen) to generate linked verse poetry in a task inspired by verse capping exercises from the Haikai no renga tradition. From an existing seed verse (maeku), models were asked to generate two additional verses that first extended the seed verse and then pivoted its meaning. We experimentally manipulated the linking modality: in one condition, models linked verses entirely via text generation; in a second condition, models had no direct access to previous verse text and instead generated images to pass meaning to the next verse. Compared to text-based generation, visual linking produced verses with greater lexical density, larger pivots from the seed verse in terms of lexical overlap and embedding distance, and a more dynamic, narrative writing style (e.g., more verbs, fewer articles). The results suggest that visual linking may be a promising direction for enabling creative divergence in LLM generation tasks.