arXiv AI
Oct 2

Hob-VL: A Benchmark for Visually Grounded Boolean Reasoning

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.

By Yuzhou Wang, Emile Anand, Ijay Narang
arXiv AI
Aug 24

CFM: Language-aligned Concept Foundation Model for Vision

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.

By Kai Wittenmayer, Sukrut Rao, Amin Parchami-Araghi, Bernt Schiele, Jonas Fischer
arXiv Machine Learning
Jun 29

COCOLogic-V2: Identifying Logical Inconsistencies via Truly Hard-Negatives

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.

By David Steinmann, Antonia W\"ust, Kristian Kersting, Wolfgang Stammer