arXiv Computer Vision
BRUCE: Benchmarking Robustness Under Corruption Escalation for Scientific Vision-Language Reasoning
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Jun 24
How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations
Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors and structural distortions, thereby introducing uncertainty into the reasoning task.
arXiv AI
Jun 9
Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?
arXiv:2606. 08063v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions.
arXiv Computer Vision
Sep 1
Think, Look, and Revise: Inconsistency-Aware Visual Self-Correction in MLLMs
arXiv:2608.29374v1 Announce Type: new Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs)...
arXiv Computer Vision
Sep 2
Beyond Blind Compliance: Benchmarking Task Verification in OCR Reasoning
arXiv:2609.00232v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on OCR-centric document understanding and text-rich visual reasoning benchmar...
arXiv Computer Vision
22h ago
Towards Characterizing Scientific Image Utility and Upgradability
arXiv:2606.03401v2 Announce Type: replace Abstract: Scientific images function as critical evidence in research communication, yet their integrity faces unprecedented threats from AI-generated conten...
arXiv AI
Jul 1
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.