arXiv Machine Learning

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

arXiv:2602. 22284v3 Announce Type: replace Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.

arXiv AI

Jun 4

UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD

arXiv:2606. 05058v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models.

By Jingyuan Chen, Sheng Jin, Haopeng Sun, Wentao Liu, Chen Qian

llms nlp multimodal benchmarks

arXiv Machine Learning

Jun 25

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.

By Xiuwei Chen, Wentao Hu, Hanhui Li, Yongxin Wang Jun Zhou, Zisheng Chen, Meng Cao, Yihan Zeng, Kui Zhang, Yu-Jie Yuan, Jianhua Han, Hang Xu, Xiaodan Liang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 19

CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation

arXiv:2605. 10873v2 Announce Type: replace-cross Abstract: Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented across datasets, modalities, and metrics.

By Anna C. Doris, Jacob Thomas Sony, Ghadi Nehme, Era Syla, Amin Heyrani Nobari, Faez Ahmed

multimodal benchmarks

arXiv Machine Learning

Jun 16

DLWM: Diverse Latent World Models for Efficient Multimodal Reasoning

arXiv:2606. 15160v1 Announce Type: cross Abstract: Reasoning capabilities of multimodal large language models (MLLMs) have improved considerably in recent years.

By David Huang, Lianlei Shan

llms diffusion reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jun 6

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

arXiv:2606. 05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks.

By Jiateng Liu, Bingxuan Li, Zhenhailong Wang, Rushi Wang, Kaiwen Hong, Cheng Qian, Jiayu Liu, Denghui Zhang, Katherine Driggs-Campbell, Manling Li, Heng Ji

llms agents computer-vision multimodal benchmarks

arXiv AI

Aug 6

MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding

arXiv:2604. 00513v3 Announce Type: replace-cross Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention.

By Junxian Wu, Chenghan Fu, Zhanheng Nie, Daoze Zhang, Bowen Wan, Wanxian Guan, Chuan Yu, Jian Xu, Bo Zheng

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 20

DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings

arXiv:2607. 15418v1 Announce Type: new Abstract: We introduce DrawingVQA, the first benchmark designed to evaluate multimodal large language models (MLLMs) on real-world construction drawings -- a core media in architecture, civil, and many other engineering practices.

By Yoonhwa Jung, Junryu Fu, Mani Golparvar-Fard

llms multimodal benchmarks

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision 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

arXiv AI

1d ago

MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems

arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.

By Penghao Yin, Haomin Wang, Qihong Tang, Xiaoye Qu, Hongjie Zhang, Xiao-Ping Zhang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 12

M*: A Modular, Extensible, Serving System for Multimodal Models

arXiv:2606. 12688v1 Announce Type: cross Abstract: We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors.

By Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin, Rohan Sanda, Steven Gao, Mark Horowitz, Luke Zettlemoyer, Olivia Hsu, Jure Leskovec, Baris Kasikci, Stephanie Wang

llms diffusion robotics efficiency multimodal

arXiv AI

Jun 2

DenseMLLM: Standard Multimodal LLMs for Dense Prediction

arXiv:2602. 14134v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding.

By Yi Li, Hongze Shen, Lexiang Tang, Xin Li, Xinpeng Ding, Yinsong Liu, Deqiang Jiang, Xing Sun, Xiaomeng Li

llms computer-vision multimodal benchmarks

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