The paper examines how humans and multimodal large language models (MLLMs) generate associative descriptions for abstract visual stimuli that become harder to name. Using two datasets—SilCC, which increases difficulty by combining abstract shapes, and PixCC, which adds visual noise—the study measures originality and effectiveness of the generated descriptions. Findings show that humans produce more diverse, semantically distant, and novel descriptions than MLLMs, and that as difficulty rises, human effectiveness drops in SilCC while they shift toward low-level visual descriptions in PixCC.
arXiv:2603. 19087v2 Announce Type: replace Abstract: Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing.
The paper introduces the Novel Visual References Dataset (NVRD), comprising 19,176 images of 90 new visual concepts with up to 20 perturbed versions each, designed to test how vision‑language models and humans map novel visual references to language. The authors evaluate three open‑source and two closed‑source VLMs against 2,400 human judgments, finding that models struggle to learn new concepts that contradict pre‑training knowledge and tend to overgeneralize labels to stimuli humans reject. NVRD is released as a benchmark for studying visual concept learning in both humans and machines.
arXiv:2608. 06501v1 Announce Type: new Abstract: Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals are scarce compared with accuracy-oriented tasks.
The paper introduces VMetaphor-Bench, a benchmark for evaluating visual metaphor generation in text-to-image models, comprising 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. It proposes a hybrid evaluation framework using a multiple-choice question protocol and dimension-based scoring to assess metaphorical fidelity. Experiments on 11 T2I models show that even top proprietary models struggle with compositional structuring and cross-domain mapping, underscoring the need for further research in this area.
Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However, words are effective as communication devices because they compress rich experiential variation into shared conventions, potentially obscuring hidden individual and cultural differences in how concepts are mentally represented.
arXiv:2605.05593v2 Announce Type: replace Abstract: Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode...
arXiv:2502.16435v5 Announce Type: replace-cross Abstract: Humans develop perception through a bottom-up hierarchy: from basic primitives and Gestalt principles to high-level semantics. Current Multim...
The paper introduces Humanity's Sixth Sense (HSS), a benchmark designed to evaluate intuitive visual reasoning in multimodal large language models. HSS covers a wide range of image and video inputs, organizes them under a structured taxonomy, and pairs each with human‑written prompts that probe implicit temporal, spatial, social, and abstract cues that humans can infer at a glance. Current state‑of‑the‑art models, including GPT‑6‑astra, achieve far lower accuracy than humans (53.6 % vs. 93.1 %), highlighting a significant gap in intuitive visual understanding.
The study investigates whether multimodal large language models (MLLMs) report bistable images, like the duck‑rabbit, in a manner similar to humans. Using the LLaVA family, researchers examined two dimensions: modulability (the influence of visual cues and linguistic priors) and exclusivity (whether responses commit to a single interpretation). Results show that both visual and linguistic manipulations shift reports in human‑consistent ways while maintaining predominantly exclusive responses, driven by competing image‑token representations and distinct bottom‑up and top‑down pathways.
The paper investigates whether vision‑language models (VLMs) can communicate visually in ways similar to humans. It reports that simple visual symbols, such as a red heart, can unexpectedly steer VLMs toward recursive solutions in unrelated tasks, revealing a generation‑interpretation gap where models produce intuitive shapes that do not effectively influence other models. In more complex communication games, VLMs can creatively use visuals to convey abstract concepts, though some messages remain unintuitive to humans, highlighting both the potential and the challenges of visual communication between humans and VLMs.
arXiv:2608. 19437v1 Announce Type: cross Abstract: Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance is evolving on open-ended tasks, where creativity, originality and diversity may matter as much as quality.
The paper introduces Percept-V, a dataset of 6,000 program-generated images across 30 domains designed to test simple visual perception skills from the TVPS-4 framework. Experiments show that state‑of‑the‑art multimodal large language models perform poorly compared to humans, especially as image complexity increases, and that fine‑tuning yields only limited generalization to related datasets. The study highlights specific perception skills that remain challenging for current models.