arXiv AI

Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains

arXiv:2608. 09873v1 Announce Type: cross Abstract: We introduce Sci-VBench, a comprehensive benchmark for evaluating knowledge- and reasoning-intensive video generation across scientific domains.

arXiv AI

Jun 26

Confidence-Aware Tool Orchestration for Robust Video Understanding

arXiv:2606. 26904v1 Announce Type: cross Abstract: Video reasoning language models implicitly assume that every input frame is equally reliable.

By Yangfan He, Yujin Choi, Jaehong Yoon

llms agents robotics benchmarks

arXiv AI

Jun 30

SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents

arXiv:2603. 29139v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visualization (SciVis) tasks.

By Kuangshi Ai, Haichao Miao, Kaiyuan Tang, Nathaniel Gorski, Jianxin Sun, Guoxi Liu, Helgi I. Ingolfsson, David Lenz, Hanqi Guo, Hongfeng Yu, Teja Leburu, Michael Molash, Bei Wang, Tom Peterka, Chaoli Wang, Shusen Liu

llms agents multimodal benchmarks

arXiv AI

Jul 1

Are Video Reasoning Models Ready to Go Outside?

arXiv:2603. 10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion.

By Yangfan He, Changgyu Boo, Jaehong Yoon

llms robotics multimodal benchmarks

arXiv AI

Aug 10

Science Edge Evaluation: SEE the Missing Step Toward Real Scientific Discovery

arXiv:2608. 06931v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly involved in scientific discovery, yet it remains unclear whether they can support complex real laboratory science.

By Taolin Han, Yuchen Zhang, Jinghang Wang, Yun Wu, Wai Yuet Chiu, Zhaohai Li, Yifei Zhang, Jinxin Wang, Yuhao Zhou, Chen Zhao, Jiajia Li, Jiaxin Li, Qile Jin, Kewei Sun, Shuang Wu, Weiqi Zhai, Renquan Lv, Junchao Li, Ruodan Chen, Qingteng Chen, Zhibo Yang, Hu Wei, Lin Qu, Shuai Bai, Bing Zhao

llms agents 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

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

Aug 11

Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models

arXiv:2608. 09666v1 Announce Type: new Abstract: Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images or videos, which is computationally expensive.

By Shulin Tian, Ziqi Huang, Fan Zhang, Hongyuan Zhu, Yu Qiao, Ziwei Liu

agents diffusion benchmarks

arXiv AI

Jul 15

IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment

arXiv:2607. 12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability.

By Jinjian Wu, Jiaqi Tang, Wei Wei, Yingying Yan, Jianmin Chen, Botong Geng, Lei Zhang, Qifeng Chen

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 2

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).

By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You

llms fine-tuning multimodal benchmarks safety

Hugging Face Trending Papers

Aug 10

RAVEN-Eval: Rubric-Guided Automatic Evaluation for AI Video Generation Models Based on LMM Preference Judgement

AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-art AI video generation models~(AIVGMs) have become increasingly difficult to discern using conventional evaluation criteria, such as visual fidelity and semantic instruction following.

diffusion benchmarks

arXiv AI

Jul 14

Evidence-Backed Video Question Answering

arXiv:2607. 11862v1 Announce Type: cross Abstract: Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding.

By Shijie Wang, Honglu Zhou, Ziyang Wang, Ran Xu, Caiming Xiong, Silvio Savarese, Chen Sun, Juan Carlos Niebles

llms computer-vision nlp fine-tuning benchmarks

arXiv AI

2d ago

MedClaw: Heuristic Agent Harness for Long-Horizon Surgical Video Reasoning

arXiv:2608. 14015v1 Announce Type: cross Abstract: Understanding tens-of-minutes surgical videos requires long-horizon temporal reasoning, answering what happens before, after, or across stages of a procedure by grounding the question in visual evidence spread across time.

By Yingying Fan, Penghui Du, Leyan Zhu, Runze He, Zimeng Wu, Yuxuan Zhang, Liang Chen, Jiahao Xie, Jiangtang Wang, Shuai Shao, Anchao Yang, Yutong Bai, Yan Wang

llms agents fine-tuning efficiency multimodal benchmarks

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