{"slug": "computational-humor-with-multimodal-llms-methods-datasets-evaluation-and", "title": "Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges", "summary": "A new survey from arXiv (2607.19011v1) finds that multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent. The survey organizes the literature using a capability-centric hierarchy spanning recognition, interpretation and reasoning, and generation, tracing the shift from task-specific fusion models to large-model approaches. Key barriers to progress include shortcut-prone evaluation, limited cultural and narrative coverage, weak evidence grounding, and unresolved safety and ownership concerns.", "body_md": "arXiv:2607.19011v1 Announce Type: new\nAbstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent rather than literal scene description. This survey focuses on visual humor understanding in single-image and multi-panel artifacts, while treating humor generation as an emerging downstream frontier. We position the literature against prior humor, sarcasm, and general MLLM surveys and organize it using a capability-centric hierarchy spanning recognition, interpretation and reasoning, and generation. Under this lens, we synthesize benchmark design, evaluation protocols, and modeling paradigms, tracing the field's shift from task-specific fusion models to large-model approaches based on multimodal alignment, evidence-grounded reasoning, and controlled generation. We conclude by highlighting the main barriers to progress: shortcut-prone evaluation, limited cultural and narrative coverage, weak evidence grounding, and unresolved safety and ownership concerns.", "url": "https://wpnews.pro/news/computational-humor-with-multimodal-llms-methods-datasets-evaluation-and", "canonical_source": "https://www.machinebrief.com/news/computational-humor-with-multimodal-llms-methods-datasets-ev-2ub6", "published_at": "2026-07-22 04:00:00+00:00", "updated_at": "2026-07-22 04:09:37.651014+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "computer-vision", "natural-language-processing", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/computational-humor-with-multimodal-llms-methods-datasets-evaluation-and", "markdown": "https://wpnews.pro/news/computational-humor-with-multimodal-llms-methods-datasets-evaluation-and.md", "text": "https://wpnews.pro/news/computational-humor-with-multimodal-llms-methods-datasets-evaluation-and.txt", "jsonld": "https://wpnews.pro/news/computational-humor-with-multimodal-llms-methods-datasets-evaluation-and.jsonld"}}