arXiv AI June 8, 2026 By Xinlei Yu, Zhangquan Chen, Yongbo He, Tianyu Fu, Guanting Dong, Cheng Yang, Chengming Xu, Yue Ma, Xiaobin Hu, Zhe Cao, Jie Xu, Guibin Zhang, Jiale Tao, Jiayi Zhang, Siyuan Ma, Kaituo Feng, Haojie Huang, Youxing Li, Ronghao Chen, Huacan Wang, Chenglin Wu, Zikun Su, Xiaogang Xu, Kelu Yao, Kun Wang, Chen Gao, Yue Liao, Ruqi Huang, Tao Jin, Zhucun Xue, Cheng Tan, Jiangning Zhang, Wenqi Ren, Yanwei Fu, Yong Liu, Yu Wang, Xiangyu Yue, Yu-Gang Jiang, Shuicheng Yan

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

Read the original on arXiv AI →

arXiv:2604. 02029v2 Announce Type: replace Abstract: Latent space is rapidly emerging as a native substrate for language-based models.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

diffusion

arXiv AI

Jun 12

LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

arXiv:2602. 00462v4 Announce Type: replace-cross Abstract: Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM.

By Benno Krojer, Shravan Nayak, Oscar Ma\~nas, Vaibhav Adlakha, Desmond Elliott, Siva Reddy, Marius Mosbach

llms rag multimodal safety

arXiv AI

Jun 2

Latent Collaboration in Multi-Agent Systems

arXiv:2511. 20639v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence.

By Jiaru Zou, Ruizhong Qiu, Gaotang Li, Xiyuan Yang, Katherine Tieu, Pan Lu, Ke Shen, Hanghang Tong, Yejin Choi, Jingrui He, James Zou, Mengdi Wang, Ling Yang

llms rag agents diffusion benchmarks

arXiv AI

Jul 14

Enabling Agents to Communicate Entirely in Latent Space

arXiv:2511. 09149v5 Announce Type: replace-cross Abstract: While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint.

By Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying

llms agents diffusion fine-tuning

arXiv AI

Jun 16

Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate

arXiv:2606. 16360v1 Announce Type: cross Abstract: Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead.

By Hanyu Lin, Min Cai, Jiawei Wen, Haodi Zhang

llms

arXiv AI

Jun 17

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

arXiv:2606. 17924v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation.

By Bochen Yang, Lianlei Shan

llms diffusion reinforcement-learning robotics multimodal benchmarks

arXiv Machine Learning

Jul 28

LanteRn: Latent Visual Structured Reasoning

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

llms rag diffusion reinforcement-learning fine-tuning multimodal benchmarks

Related stories

LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

Latent Collaboration in Multi-Agent Systems

Enabling Agents to Communicate Entirely in Latent Space

Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

LanteRn: Latent Visual Structured Reasoning