SLVR (Structured Latent Visual Reasoning) is a training framework that blends explicit chain-of-thought with latent reasoning for multimodal large language models. It organizes reasoning into typed latent stages—planning, grounding, evidence selection, and integration—each supervised by corresponding signals such as plans, bounding boxes, visual evidence, and final rationales. Built on Qwen2.5‑VL‑7B, SLVR consistently improves performance on multimodal reasoning benchmarks, achieving significant gains on MMVP, BLINK Relation, V*, MathVista, and ChartQA.
By Albert Gao, Bing Xue, Andrea Zanette
ReGuLaR introduces a relation‑grounded latent reasoning framework for large vision‑language models, explicitly anchoring latent states to key objects and their inter‑object relations. It employs a training‑time ReGFormer to focus reasoning on question‑relevant visual evidence, while inference proceeds without the ReGFormer. The authors also create the RGROUNDING‑351K dataset and demonstrate that ReGuLaR outperforms existing methods across diverse benchmarks, achieving state‑of‑the‑art results.
By Zihu Wang, Karthik Somayaji N. S, Hejia Geng, Peng Li
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
arXiv:2603. 25629v2 Announce Type: replace-cross Abstract: While language reasoning models excel in many tasks, visual reasoning remains challenging for current large multimodal models (LMMs).
By Andr\'e G. Viveiros, Nuno Gon\c{c}alves, Matthias Lindemann, Andr\'e Martins
arXiv:2605.08802v4 Announce Type: replace
Abstract: Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to...
By Ziyang Ding, Linjian Meng, Yiming Wu, Yuhan Li, Yuhao Liu, Zhen Zhao
arXiv:2604.07518v2 Announce Type: replace
Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add...
By Mengdan Zhu, Senhao Cheng, Liang Zhao
arXiv:2602.05359v3 Announce Type: replace
Abstract: The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often...
By Yiming Zhang, Qiangyu Yan, Borui Jiang, Kai Han
The paper introduces LIRSeg, a method that replaces explicit Chain-of-Thought reasoning in multimodal large language models with a compact set of learnable latent tokens for reasoning segmentation. LIRSeg is trained in two stages—spatial alignment and GRPO—while employing extreme-advantage sampling, decoupled exploration-stability updates, and latent diversity amplification to enhance token informativeness. Experiments show that LIRSeg improves segmentation accuracy and reasoning efficiency, achieving significant gIoU gains over the VisionReasoner baseline and reducing reasoning tokens by about 16×.
By Tianhang Guo, Yulin He, Wei Chen, Wenjuan Zhou, Yuhang Li, Xinbiao Gan
arXiv:2606. 00562v1 Announce Type: cross Abstract: The emerging paradigm of "thinking with images" embeds visual states into intermediate reasoning steps, defining a new frontier for Vision-Language Models.
By Dongchen Lu, Zhimo Li, Mao Shu, Huo Cao
arXiv:2605.18641v2 Announce Type: replace
Abstract: Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generat...
By Dongyao Zhu, Zhen Wang, Xi Xiao, Han Jiang, Saeed Vahidian, Wei-Lun Chao, Tanya Berger-Wolf, Yu Su, Raju Vatsavai, Jianyang Gu
The paper introduces CAVE, a structured credit assignment method that enhances Vision‑Language Models by evaluating intermediate reasoning steps through belief updates, evidence acquisition, and adaptive focus control. CAVE is built on a GRPO framework and guides models to optimize each action, improving reliability in fragmented visual reasoning. The authors also present TRACER‑Bench, a new benchmark covering nonlocal and semantically confusable reasoning dimensions, and show that CAVE boosts performance on both public datasets and this benchmark while maintaining strong general multimodal results.
By Tengda Guo, Jie Leng, Hanlei Li, Yaoyuan Liang, Qingyue Zhang, Dian Yang, Mingyu Zhang, Yuhua Fu, Shao-Lun Huang
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