arXiv AI
5d ago

Skip the Talk, Re-Focus on Vision: Latent Reasoning for Reasoning Segmentation in Multimodal Large Language Models

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 Computation and Language
Sep 4

Imagination Helps Visual Reasoning, But Not Yet in Latent Space

The paper investigates latent visual reasoning in multimodal large language models, treating input, latent tokens, and final answer as a causal chain. Causal mediation analysis reveals two disconnections: latent tokens largely ignore input perturbations, and changes to latent tokens minimally affect the final answer, indicating limited causal influence. Probing shows latent tokens encode little visual information and are highly similar, leading the authors to propose CapImagine, an explicit text‑based imagination approach that outperforms latent‑space baselines on vision‑centric benchmarks.

By You Li, Chi Chen, Yanghao Li, Fanhu Zeng, Kaiyu Huang, Jinan Xu, Maosong Sun
arXiv Machine Learning
Aug 21

Scaffolding Minds: Optimizing Latent Visual Target Representations for Multimodal Reasoning

arXiv:2608. 19669v1 Announce Type: cross Abstract: Latent reasoning has advanced multimodal reasoning through a two-stage training paradigm: (1) a helper image is encoded into latent tokens to teach visual chain-of-thought during a supervised fine-tuning (SFT) stage, and (2) these latent tokens are further refined with reward feedback during a reinforcement learning (RL) stage.

By Haoqiang Kang, Yinpeng Chen, Luyang Liu, Jesper Sparre Andersen, Abhijit Ogale, Baochen Sun, Lichan Hong, Ed H. Chi
Hugging Face Trending Papers
Aug 20

Scaffolding Minds: Optimizing Latent Visual Target Representations for Multimodal Reasoning

Latent reasoning has advanced multimodal reasoning through a two-stage training paradigm: (1) a helper image is encoded into latent tokens to teach visual chain-of-thought during a supervised fine-tuning (SFT) stage, and (2) these latent tokens are further refined with reward feedback during a reinforcement learning (RL) stage. In this paper, we identify two key limitations of this framework, one in each stage.