arXiv AI June 19, 2026 By Bhavan Vasu, Rajesh Mangannavar

GLARE: A Natural Language Interface for Querying Global Explanations

arXiv:2606. 19735v1 Announce Type: new Abstract: While global explanations are crucial for understanding vision models across datasets, classes, and decision contexts, their complex and monolithic nature often hinders practical exploration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Sep 23

Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?

arXiv:2609.26208v1 Announce Type: new Abstract: Data visualization is central to analytical reasoning, but real-world analysis increasingly requires language-driven interactive interfaces rather than...

By Mizanur Rahman, Aaryaman Kartha, Enamul Hoque Prince

llms nlp benchmarks

arXiv Machine Learning

Jun 8

OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models

arXiv:2606. 07180v1 Announce Type: cross Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine learning research.

By Arthur Hoarau, Chenrui Zhu, Vu Linh Nguyen

computer-vision benchmarks

arXiv Computer Vision

Aug 25

Boosting Knowledge-based Visual Question Answering with Structured Context Reasoning

arXiv:2608.21431v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information...

By Qiyou Liu, Yong Zhang, Jianjie Luo, Zhenguo Yang, Yi Yu

llms nlp efficiency multimodal benchmarks

arXiv AI

Jun 6

Personal AI Agent for Camera Roll VQA

arXiv:2606. 05275v1 Announce Type: cross Abstract: We study the personal camera roll visual question answering setting.

By Thao Nguyen, Krishna Kumar Singh, Donghyun Kim, Yong Jae Lee, Yuheng Li

agents nlp

arXiv AI

Jul 13

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.

By Carmen Quiles-Ram\'irez, Leticia L. Rodr\'iguez, Nicol\'as Martorell, Natalia D\'iaz-Rodr\'iguez

llms multimodal benchmarks

arXiv Machine Learning

Jul 2

AdaBoosting Text Prompts for Vision-Language Models

arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.

By Seokhee Jin, Changhwan Sung, Sunung Mun, Hoyoung Kim, Jungseul Ok