arXiv:2610.20083v1 Announce Type: new Abstract: Visual world knowledge is a core capability of multimodal large language models (MLLMs), underpinning knowledge-intensive visual question answering and...
Hob‑VL is a benchmark for visually grounded Boolean reasoning that tests models on two tasks: determining whether a Boolean rule holds in an image and identifying the unique object that satisfies a Boolean description. It contains 6,000 balanced Yes/No questions built from ten visual statements across 1,000 generated scenes and 46 labeled photographs, plus 1,000 object‑identification questions on the same photographs. The questions are designed to challenge reasoning with misleading local cues, nested logical operations, and both symbolic and natural‑language presentations, revealing significant gaps in current model performance.
arXiv:2607. 10666v1 Announce Type: cross Abstract: Deploying AI-based visual inspection in manufacturing is hard because requirements change often, new defect types appear, and large labeled datasets are rarely available.
arXiv:2508. 10956v3 Announce Type: replace-cross Abstract: Inspired by human categorization, visual reasoning about object properties, such as physical attributes and functions, involves identifying and recognizing low-level details and higher-level abstractions.
The paper introduces CFM, a language‑aligned concept foundation model for vision that generates fine‑grained, human‑interpretable concepts with spatial grounding. By pairing CFM with a strong semantic foundation model, it provides explanations for downstream tasks such as classification, segmentation, and captioning. The authors also analyze local co‑occurrence of concepts to define relationships, improving concept naming and yielding richer explanations while maintaining competitive performance.
arXiv:2606. 28194v1 Announce Type: new Abstract: While interpretable models such as concept bottleneck models (CBMs) and program synthesis methods enable verification of model decisions, their evaluation is typically limited to simple tasks, leaving complex reasoning on real-world images largely unexplored.
arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...
arXiv:2608.23518v1 Announce Type: new Abstract: Vision-Language Models (VLMs) achieve strong performance in visual reasoning tasks, but it remains unclear whether they understand visual relations, or...
arXiv:2609.38362v1 Announce Type: new Abstract: Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining dis...
GraphVerse is a new benchmark for multimodal large language models that tests visual graph reasoning by combining perception, graph-based visual inputs, and multi-step reasoning. It introduces Graph-centric Image Editing strategies to actively challenge models while preserving semantics, and offers a VGR-Score metric that evaluates reasoning quality beyond simple answer accuracy. Experiments show current models have limitations in VGR and demonstrate the effectiveness of GraphVerse for broader multimodal reasoning tasks.
arXiv:2606. 31976v1 Announce Type: new Abstract: Human-labeled data are widely used as reference annotations in ML, despite known variability across annotators in many expert-driven domains.
The paper introduces Self-RefineVQA, a training‑free, inference‑time framework that lets a single Vision‑Language Model iteratively generate, critique, and refine its own answers for table visual question answering. By forming a closed self‑feedback loop guided by prompts, the method corrects common reasoning errors such as spatial misalignment and logical hallucinations without requiring additional labeled data or model updates. Experiments on tabular VQA benchmarks show consistent accuracy gains across monolingual and cross‑lingual settings, especially for lightweight models, while keeping inference cost linear and eliminating the need for expensive supervised fine‑tuning.