arXiv AI June 2, 2026 By Xia Hu, Zhenrui Yue, Brian Potetz, Howard Zhou, Leonidas Guibas, Chun-Ta Lu, Zhicheng Wang

The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space

arXiv:2605. 09883v2 Announce Type: replace-cross Abstract: As current Multimodal Large Language Models rapidly saturate canonical visual reasoning benchmarks, a key question emerges: do these strong scores genuinely reflect robust visual understanding?

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

Jun 25

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning.

multimodal safety

arXiv Computer Vision

Aug 26

Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models

arXiv:2510.13394v4 Announce Type: replace Abstract: Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, au...

By Xinmiao Huang, Qisong He, Zhenglin Huang, Boxuan Wang, Zhuoyun Li, Guangliang Cheng, Yi Dong, Xiaowei Huang

llms agents robotics multimodal benchmarks

arXiv AI

Jun 26

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

arXiv:2606. 26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning.

By Zhixing Li, Yinan Yu

multimodal safety

arXiv Computation and Language

Sep 15

Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models

arXiv:2609.14779v1 Announce Type: new Abstract: Performing deliberate mathematical reasoning in visual contexts is a hallmark of advanced Multimodal Large Language Models (MLLMs) and requires a sophi...

By Mingze Yin, Xiaohan Wang, Dian Li, Haichao Yao, Yilin Zhao, Youjun Chen, Gang Liu, Jintai Chen, Yiheng Zhu, Chang-Yu Hsieh, Aimin Pan

llms multimodal benchmarks

Hugging Face Trending Papers

Sep 13

Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models

Performing deliberate mathematical reasoning in visual contexts is a hallmark of advanced Multimodal Large Language Models (MLLMs) and requires a sophisticated synthesis of perceptual grounding and sy...

llms multimodal benchmarks

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Related stories

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

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models

Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models