arXiv AI June 2, 2026 By Anglin Liu, Ruichao Chen, Yi Lu, Hongxia Xu, Jintai Chen

Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training

arXiv:2601. 23220v2 Announce Type: replace-cross Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geometric blindness.

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 AI

Jun 24

E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis

arXiv:2606. 23888v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) show great promise in volumetric medical report generation, they frequently suffer from visual hallucinations and a lack of grounding in 3D CT data.

By Sijing Li, Zhongwei Qiu, Zhuoya Wang, Boxiang Yun, Zhenyu Yi, Jianwei Xu, Wenqiao Zhang, Yingda Xia, Ling Zhang

llms reinforcement-learning fine-tuning multimodal safety

arXiv AI

Jun 24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.

By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv AI

Sep 15

Beyond Generation and Accuracy: Diagnosing and Enhancing Visual Chain-of-Thought for Geometry Problem Solving

arXiv:2609.12606v2 Announce Type: replace Abstract: While multimodal reasoning has advanced rapidly, solving complex geometry problems critically hinges on active visual assistance, such as construct...

By Zhitong Dong, Jicai Pan, Yingguo Gao, Jingting Ding, Hao Chen, Jinjie Gu

agents reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

3d ago

GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions

arXiv:2609.38285v1 Announce Type: cross Abstract: Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Add...

By Hongbo Wang, Zihan Lin, Wenkui Yang, Shiran Ge, Yuang Ai, Jie Cao, Huaibo Huang, Ran He

llms agents robotics fine-tuning multimodal

arXiv AI

Jul 8

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.

By Franciskus Xaverius Erick, Johanna Paula M\"uller, Bernhard Kainz

llms rag fine-tuning efficiency multimodal safety

arXiv AI

Jul 21

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering

arXiv:2604. 09757v2 Announce Type: replace-cross Abstract: Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded once as static context, and subsequent inference is dominated by language.

By Suyang Xi, Songtao Hu, Yuxiang Lai, Wangyun Dan, Yaqi Liu, Shansong Wang, Xiaofeng Yang

llms rag nlp fine-tuning benchmarks

One email a morning, machine-written

Related stories

E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

Beyond Generation and Accuracy: Diagnosing and Enhancing Visual Chain-of-Thought for Geometry Problem Solving

GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering