arXiv Machine Learning

FAU at ImageCLEF 2026 Task on Multimodal Reasoning Robust Candidate Scoring and Concise Multilingual Visual Answering

arXiv:2608. 01664v1 Announce Type: cross Abstract: We present our ImageCLEF 2026 Multimodal Reasoning system for the Visual Multiple Choice Question Answering (Visual MCQ) and Visual Open Question Answering (Visual OpenQA) subtasks.

arXiv AI

Aug 5

CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning

arXiv:2608. 02833v1 Announce Type: cross Abstract: Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate visual grounding and coherent reasoning chains.

By Xuehang Guo, Pingyue Zhang, Ruiyi Zhang, Zhenhailong Wang, Hanrui Lyu, Heng Ji, Tong Sun, Qingyun Wang, Manling Li

llms nlp multimodal benchmarks

arXiv AI

Jun 11

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

arXiv:2603. 09715v2 Announce Type: replace Abstract: Visual instruction tuning is crucial for improving vision-language large models (VLLMs).

By Peng Sun, Yi Yang, Huawen Shen, Yi Ban, Tianfan Fu, Yanbo Wang, Yuqiang Li

fine-tuning multimodal

arXiv AI

Jul 13

Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026

arXiv:2607. 09623v1 Announce Type: cross Abstract: We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA).

By Nirjhar Das, Md. Al-Mamun Provath

llms agents nlp multimodal benchmarks

arXiv AI

Jun 10

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

arXiv:2606. 10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reasoning remains largely unexplored.

By Syed Wasiq, Syed Mohamad Tawseeq, Yashwant Pravinrao Bangde, Debaditya Roy

llms nlp multimodal benchmarks

arXiv AI

Jun 19

Vero: An Open RL Recipe for General Visual Reasoning

arXiv:2604. 04917v3 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks?

By Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Aug 7

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.

By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

llms nlp multimodal benchmarks

arXiv AI

Jul 9

A Study of Commonsense Reasoning over Visual Object Properties

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.

By Abhishek Kolari, Mohammadhossein Khojasteh, Yifan Jiang, Floris den Hengst, Filip Ilievski

llms nlp multimodal benchmarks

arXiv AI

Jun 17

MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning

arXiv:2606. 17888v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has extended from purely linguistic domains to multimodal scenarios; however, existing approaches often treat visual inputs as homogeneous or auxiliary signals, failing to capture the intricate and sample-specific dependencies between text and images in mathematical problem-solving.

By Wanshi Xu, Haokun Zhao, Haidong Yuan, Songjun Cao, Long Ma

multimodal safety

arXiv AI

Jun 9

Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text

arXiv:2606. 09585v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs).

By Yutong Bian, Dongjie Cheng, Heming Xia, Yongqi Li, Wenjie Li

llms multimodal benchmarks

arXiv AI

Jun 11

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.

By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi

llms fine-tuning multimodal benchmarks safety

arXiv AI

2d ago

BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA

arXiv:2603. 28026v2 Announce Type: replace Abstract: Multimodal multiple-choice question answering (MCQA) provides a standardized and objectively measurable setting for evaluating vision-language models (VLMs).

By Taeyun Roh, Suhyeong Park, Dongyoung Lee, Eunyeong Jo, Wonjune Jang, Junha Jung, Jaewoo Kang

llms nlp efficiency multimodal benchmarks safety

arXiv AI

Jun 11

MSUE: Multi-Modal Soccer Understanding Expert

arXiv:2606. 12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge.

By Litao Li, Yibo Yu, Yufeng Hu, Zhuo Yang, Jiali Wen, Yixin Chen, Yixi Zhou

llms rag nlp fine-tuning multimodal benchmarks

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