The paper introduces Visual Metaphor Transfer (VMT), a task that requires models to extract the abstract ‘creative essence’ from a reference image and apply it to a new target subject. It proposes a multi‑agent framework based on Conceptual Blending Theory, using a Schema Grammar to separate relational invariants from visual entities. The system includes perception, transfer, generation, and diagnostic agents, and experimental results show it outperforms state‑of‑the‑art baselines in metaphor consistency, analogy appropriateness, and visual creativity.
By Yu Xu, Yuxin Zhang, Lin Gao, Oliver Deussen, Tong-Yee Lee, Fan Tang
arXiv:2510. 04120v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing.
By Fengying Ye, Shanshan Wang, Lidia S. Chao, Derek F. Wong
arXiv:2601. 03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)' reasoning pathways, given that their training data contain a large number of metaphors.
By Zhibo Hu, Chen Wang, Yanfeng Shu, Hye-young Paik, Liming Zhu
arXiv:2609.34187v2 Announce Type: replace-cross
Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they c...
By Julia Witte Zimmerman, Calla G. Beauregard, Tabia Tanzin Prama, Parisa Suchdev, Kathryn Cramer, Elisabeth Kollrack
Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification.
arXiv:2606. 04057v1 Announce Type: cross Abstract: Large language models (LLMs) now generate substantial production code, often for tasks with multiple valid algorithmic solutions.
By Akanksha Narula, Mofasshara Binte Rafique, Laurent Bindschaedler