arXiv Machine Learning

Assessing Adversarial Robustness of Latent Reasoning Models

arXiv AI

Aug 17

Think in Latent, Explain in Language: Self-Explainable Latent Reasoning

arXiv:2608. 13570v1 Announce Type: cross Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings.

By Dayuan Zhao, Shengcao Cao, Yu-Xiong Wang, Liang-Yan Gui

llms rag diffusion multimodal safety

arXiv Computer Vision

Sep 1

LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck

arXiv:2606.13061v3 Announce Type: replace Abstract: Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. D...

By Peixi Wu, Biao Yang, Feipeng Ma, Bosong Chai, Bo Lin, Wei Yuan, Fan Yang, Tingting Gao, Hebei Li, Xiaoyan Sun

rag diffusion efficiency multimodal

arXiv Machine Learning

Jun 2

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention

arXiv:2606. 01243v1 Announce Type: cross Abstract: Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT).

By Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu

llms safety

arXiv Machine Learning

Aug 11

Are Latent Reasoning Models Easily Interpretable?

arXiv:2604. 04902v2 Announce Type: replace Abstract: Latent reasoning models (LRMs) have attracted significant research interest due to their low inference cost (relative to explicit reasoning models) and theoretical ability to explore multiple reasoning paths in parallel.

By Connor Dilgren, Sarah Wiegreffe

benchmarks safety

arXiv AI

Jun 30

Invariant Reasoning Directions in Latent Trajectories of Language Models

arXiv:2606. 29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains poorly understood.

By Arun Vignesh Malarkkan, Manan Roy Choudhury, Utkarsh Byahut, Yash Ravindra Charde, Vivek Gupta, Yanjie Fu

llms diffusion benchmarks safety

arXiv AI

5d ago

TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens

arXiv:2605.16638v2 Announce Type: replace Abstract: Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this pa...

By Jianpeng Cheng, Xian Wu, Jiangfan Zhang, Wentao Bao, Chaitanya Ahuja, Shlok Kumar Mishra, Xuanming Cui, Hanchao Yu, Yang Gao, Fan Xia, Haixing Dai, Frankie Yuan, Zihao Wang, Xiaobing Chen, Qi Guo, Shaodan Zhai, Aashu Singh, Xiangjun Fan, Jun Xiao

llms rag multimodal benchmarks

arXiv AI

Sep 1

Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models

arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigg...

By Zhenghao He, Guangzhi Xiong, Bohan Liu, Sanchit Sinha, Aidong Zhang

llms benchmarks

arXiv Machine Learning

Jun 16

DLWM: Diverse Latent World Models for Efficient Multimodal Reasoning

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

llms diffusion reinforcement-learning efficiency multimodal benchmarks

arXiv Computation and Language

Sep 1

Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning

arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...

By Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, Xiaoyu Shen

llms nlp

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

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 Machine Learning

Jun 5

Latent Reasoning with Normalizing Flows

arXiv:2606. 06447v1 Announce Type: cross Abstract: Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation.

By Guancheng Tu, Xiangjun Fu, Suhao Yu, Yao Tang, Haoqiang Kang, Lianhui Qin, Yizhe Zhang, Jiatao Gu

llms efficiency benchmarks

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