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.
Tuo Liang, Zhe Hu, Disheng Liu et al.· 0 citations
Vision-language models have achieved impressive progress, yet they still struggle with spatial intelligence–understanding where objects are, how they relate, and how space changes across viewpoints. This limitation matters for embodied AI, autonomous driving, and spatially consistent generation. Meanwhile, rapid advances in spatially enhanced VLMs have produced a scattered literature with inconsistent terminology, methods, and evaluation practices. In this survey, we provide a comprehensive and unified overview of recent advances in spatial intelligence for VLMs. We summarize core concepts behind spatial reasoning in VLMs, analyze why spatial failures occur, and organize existing solutions into a clear framework spanning prompting-based techniques, model improvements, explicit 2D cues, 3D enrichment, and data-driven strategies. We also examine how spatial ability is currently measured and report an empirical study across 37 models and 9 representative benchmarks. Our analysis highlights current best-performing approaches, clarifies when different strategies help or fail, shows the existence of performance gaps across different evaluation datasets and reveals the potential design biases in current spatial understanding benchmarks. By consolidating evidence and outlining open challenges, this survey offers a practical roadmap for building more spatially capable VLMs. We release our
evaluation code
and maintain a curated
paper repository
to support the rapidly growing research on spatial intelligence in vision-language models.
Disheng Liu, Tuo Liang, Zhe Hu et al.· Artificial Intelligence Revi...· 6 citations
Counterfactual Realignment (CoRe), a training-free framework that recovers a frozen VLA at inference time without failure data, is proposed, a training-free framework that recovers a frozen VLA at inference time without policy fine-tuning or failure-specific recovery training.
Yanyan Zhang, Disheng Liu, Kai Ye et al.· 0 citations
