arXiv Machine Learning
Sep 10

I Don't Miss You, but I Do: Self-Explanation Faithfulness of Modality Missingness in Vision-Language Models

The paper introduces an interventional protocol to assess how vision‑language models (VLMs) explain the impact of missing modalities on their predictions. By comparing the models’ self‑explanations with actual changes observed after restoring missing inputs, the study finds that VLMs routinely overstate the sufficiency of available evidence and underestimate the effect of adding back missing modalities. Across eight open‑weight VLMs and four tasks, the discrepancy between predicted and realized changes is substantial, revealing systematic mischaracterization of modality dependence.

By Aydin Javadov, Daniel Schoess, Florian von Wangenheim
arXiv AI
Jun 2

TECCI: Tricky Edits of Collected and Curated Images

arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.

By Aishwarya Agrawal, Roy Hirsch, Yasumasa Onoe, Sherry Ben, Jason Baldridge
arXiv Computer Vision
Aug 28

Aphanta: Diagnosing Task-Aligned Image-Edited Intermediates for Multimodal Reasoning

Aphanta is an automated framework that diagnoses how well image editors can produce task‑aligned visual intermediates for multimodal large language models (MLLMs). It evaluates three reasoning conditions—direct, editor‑generated, and idealized intermediate—to distinguish visual potential from practical editor performance across 20 tasks and various editor–MLLM pairs. The study finds that image editing benefits certain tasks like visual cue injection and grounding, but is less reliable for symbol‑sensitive or structural tasks, and demonstrates measurable performance gains with a Qwen pipeline.

By Hengyuan Xu, Wei Cheng, Yumeng Ji, Xuanyang Zhang, Xianfang Zeng, Gang Yu, Xingjun Ma